The document proposes an improved k-means clustering algorithm to address some limitations of the traditional k-means method. The improved algorithm handles mixed categorical and numeric data by converting categorical attributes to numeric values. It determines initial cluster centers using hierarchical clustering and chooses the optimal number of clusters k based on two new coefficients α and β. An analysis of patient record data from a healthcare database demonstrates that the improved k-means algorithm can identify an appropriate number of clusters while dealing with issues like mixed data types.
Simplicial closure and higher-order link prediction --- SIAMNS18Austin Benson
The document discusses higher-order link prediction, which aims to predict the formation of new groups or "simplices" containing more than two nodes, based on structural properties in timestamped simplex data from various domains. It finds that predicting the closure of open triangles (where a pair of nodes have interacted but not with the third) performs well, and that simply averaging the edge weights in a triangle is often a good predictor. Predicting new structures in communication, collaboration and proximity networks can provide insights beyond classical link prediction.
Cost Optimized Design Technique for Pseudo-Random Numbers in Cellular Automataijait
In this research work, we have put an emphasis on the cost effective design approach for high quality pseudo-random numbers using one dimensional Cellular Automata (CA) over Maximum Length CA. This work focuses on different complexities e.g., space complexity, time complexity, design complexity and searching complexity for the generation of pseudo-random numbers in CA. The optimization procedure for
these associated complexities is commonly referred as the cost effective generation approach for pseudorandom numbers. The mathematical approach for proposed methodology over the existing maximum length CA emphasizes on better flexibility to fault coverage. The randomness quality of the generated patterns for the proposed methodology has been verified using Diehard Tests which reflects that the randomness quality
achieved for proposed methodology is equal to the quality of randomness of the patterns generated by the maximum length cellular automata. The cost effectiveness results a cheap hardware implementation for the concerned pseudo-random pattern generator. Short version of this paper has been published in [1].
This document discusses cluster analysis and various clustering algorithms. It begins with an overview of supervised and unsupervised learning, as well as generative models. It then discusses 5 common clustering techniques: partitioning, hierarchical, density-based, grid-based, and model-based clustering. The document also covers challenges with cluster analysis such as centroid initialization, outlier handling, categorical data, the curse of dimensionality, and computational complexity. Specific clustering algorithms discussed in more detail include K-means, K-medoids, K-modes, mini-batch K-means, and scalable K-means++.
The document summarizes an analysis of predicting forest cover types using machine learning models. It describes the dataset containing over 500,000 observations of forest cover types and 12 descriptive variables. Various models were tested including decision forests, boosted decision trees, and neural networks. The best performing models on the test set were an ensemble approach blending multiple models and a one-vs-all decision forest, both achieving over 80% accuracy. Experiments were conducted using Microsoft Azure machine learning services.
An enhanced fuzzy rough set based clustering algorithm for categorical dataeSAT Journals
Abstract In today’s world everything is done digitally and so we have lots of data raw data. This data are useful to predict future events if we proper use it. Clustering is such a technique where we put closely related data together. Furthermore we have types of data sequential, interval, categorical etc. In this paper we have shown what is the problem with clustering categorical data with rough set and who we can overcome with improvement.
An enhanced fuzzy rough set based clustering algorithm for categorical dataeSAT Publishing House
This document summarizes a research paper that proposes an enhanced fuzzy rough set-based clustering algorithm for categorical data. The paper discusses problems with using traditional rough set theory to cluster categorical data when there are no crisp relations between attributes. It proposes using fuzzy logic to assign weights to attribute values and calculate lower approximations based on the similarity between sets, in order to cluster categorical data when crisp relations do not exist. The proposed method is described through an example comparing traditional rough set clustering to the new fuzzy rough set approach.
This document summarizes an academic paper that proposes an innovative modified K-Mode clustering algorithm for categorical data. The paper begins by introducing clustering algorithms and discusses existing algorithms like K-Means, K-Medoids, and K-Mode that are used for numerical and categorical data. It then describes the limitations of traditional K-Mode clustering and proposes a modified K-Mode algorithm that aims to provide better initial cluster means/modes to result in clusters with better accuracy. The paper experimentally evaluates the traditional and modified K-Mode algorithms on large datasets to compare their performance for varying data values.
The document proposes an improved k-means clustering algorithm to address some limitations of the traditional k-means method. The improved algorithm handles mixed categorical and numeric data by converting categorical attributes to numeric values. It determines initial cluster centers using hierarchical clustering and chooses the optimal number of clusters k based on two new coefficients α and β. An analysis of patient record data from a healthcare database demonstrates that the improved k-means algorithm can identify an appropriate number of clusters while dealing with issues like mixed data types.
Simplicial closure and higher-order link prediction --- SIAMNS18Austin Benson
The document discusses higher-order link prediction, which aims to predict the formation of new groups or "simplices" containing more than two nodes, based on structural properties in timestamped simplex data from various domains. It finds that predicting the closure of open triangles (where a pair of nodes have interacted but not with the third) performs well, and that simply averaging the edge weights in a triangle is often a good predictor. Predicting new structures in communication, collaboration and proximity networks can provide insights beyond classical link prediction.
Cost Optimized Design Technique for Pseudo-Random Numbers in Cellular Automataijait
In this research work, we have put an emphasis on the cost effective design approach for high quality pseudo-random numbers using one dimensional Cellular Automata (CA) over Maximum Length CA. This work focuses on different complexities e.g., space complexity, time complexity, design complexity and searching complexity for the generation of pseudo-random numbers in CA. The optimization procedure for
these associated complexities is commonly referred as the cost effective generation approach for pseudorandom numbers. The mathematical approach for proposed methodology over the existing maximum length CA emphasizes on better flexibility to fault coverage. The randomness quality of the generated patterns for the proposed methodology has been verified using Diehard Tests which reflects that the randomness quality
achieved for proposed methodology is equal to the quality of randomness of the patterns generated by the maximum length cellular automata. The cost effectiveness results a cheap hardware implementation for the concerned pseudo-random pattern generator. Short version of this paper has been published in [1].
This document discusses cluster analysis and various clustering algorithms. It begins with an overview of supervised and unsupervised learning, as well as generative models. It then discusses 5 common clustering techniques: partitioning, hierarchical, density-based, grid-based, and model-based clustering. The document also covers challenges with cluster analysis such as centroid initialization, outlier handling, categorical data, the curse of dimensionality, and computational complexity. Specific clustering algorithms discussed in more detail include K-means, K-medoids, K-modes, mini-batch K-means, and scalable K-means++.
The document summarizes an analysis of predicting forest cover types using machine learning models. It describes the dataset containing over 500,000 observations of forest cover types and 12 descriptive variables. Various models were tested including decision forests, boosted decision trees, and neural networks. The best performing models on the test set were an ensemble approach blending multiple models and a one-vs-all decision forest, both achieving over 80% accuracy. Experiments were conducted using Microsoft Azure machine learning services.
An enhanced fuzzy rough set based clustering algorithm for categorical dataeSAT Journals
Abstract In today’s world everything is done digitally and so we have lots of data raw data. This data are useful to predict future events if we proper use it. Clustering is such a technique where we put closely related data together. Furthermore we have types of data sequential, interval, categorical etc. In this paper we have shown what is the problem with clustering categorical data with rough set and who we can overcome with improvement.
An enhanced fuzzy rough set based clustering algorithm for categorical dataeSAT Publishing House
This document summarizes a research paper that proposes an enhanced fuzzy rough set-based clustering algorithm for categorical data. The paper discusses problems with using traditional rough set theory to cluster categorical data when there are no crisp relations between attributes. It proposes using fuzzy logic to assign weights to attribute values and calculate lower approximations based on the similarity between sets, in order to cluster categorical data when crisp relations do not exist. The proposed method is described through an example comparing traditional rough set clustering to the new fuzzy rough set approach.
This document summarizes an academic paper that proposes an innovative modified K-Mode clustering algorithm for categorical data. The paper begins by introducing clustering algorithms and discusses existing algorithms like K-Means, K-Medoids, and K-Mode that are used for numerical and categorical data. It then describes the limitations of traditional K-Mode clustering and proposes a modified K-Mode algorithm that aims to provide better initial cluster means/modes to result in clusters with better accuracy. The paper experimentally evaluates the traditional and modified K-Mode algorithms on large datasets to compare their performance for varying data values.
The document provides an overview of cluster analysis techniques. It discusses the need for segmentation to group large populations into meaningful subsets. Common clustering algorithms like k-means are introduced, which assign data points to clusters based on similarity. The document also covers calculating distances between observations, defining the distance between clusters, and interpreting the results of clustering analysis. Real-world applications of segmentation and clustering are mentioned such as market research, credit risk analysis, and operations management.
K-Means clustering uses an iterative procedure which is very much sensitive and dependent upon the initial centroids. The initial centroids in the k-means clustering are chosen randomly, and hence the clustering also changes with respect to the initial centroids. This paper tries to overcome this problem of random selection of centroids and hence change of clusters with a premeditated selection of initial centroids. We have used the iris, abalone and wine data sets to demonstrate that the proposed method of finding the initial centroids and using the centroids in k-means algorithm improves the clustering performance. The clustering also remains the same in every run as the initial centroids are not randomly selected but through premeditated method.
Hybridization of Bat and Genetic Algorithm to Solve N-Queens ProblemjournalBEEI
In this paper, a hybrid of Bat-Inspired Algorithm (BA) and Genetic Algorithm (GA) is proposed to solve N-queens problem. The proposed algorithm executes the behavior of microbats with changing pulse rates of emissions and loudness to final all the possible solutions in the initialization and moving phases. This dataset applied two metaheuristic algorithms (BA and GA) and the hybrid to solve N-queens problem by finding all the possible solutions in the instance with the input sizes of area 8*8, 20*20, 50*50, 100*100 and 500*500 on a chessboard. To find the optimal solution, consistently, ten run have been set with 100 iterations for all the input sizes. The hybrid algorithm obtained substantially better results than BA and GA because both algorithms were inferior in discovering the optimal solutions than the proposed randomization method. It also has been discovered that BA outperformed GA because it requires a reduced amount of steps in determining the solutions.
This document discusses different approaches to multivariate data analysis and clustering, including nearest neighbor methods, hierarchical clustering, and k-means clustering. It provides examples of using Ward's method, average linkage, and k-means clustering on poverty data to identify potential clusters of countries based on variables like birth rate, death rate, and infant mortality rate. Key lessons are that different linkage methods, distance measures, and data normalizations should be tested and that higher-dimensional data may require different variable spaces or transformations to identify meaningful clusters.
K-Means, its Variants and its ApplicationsVarad Meru
This presentation was given by our project group at the Lead College competition at Shivaji University. Our project got the 1st Prize. We focused mainly on Rough K-Means and build a Social-Network-Recommender System based on Rough K-Means.
The Members of the Project group were -
Mansi Kulkarni,
Nikhil Ingole,
Prasad Mohite,
Varad Meru
Vishal Bhavsar.
Wonderful Experience !!!
New Approach for K-mean and K-medoids AlgorithmEditor IJCATR
K-means and K-medoids clustering algorithms are widely used for many practical applications. Original k
medoids algorithms select initial centroids and medoids randomly that affect the quality of the resulting clusters and sometimes it
generates unstable and empty clusters which are meaningless.
expensive and requires time proportional to the product of the number of data items, number of clusters and the number of iterations.
The new approach for the k mean algorithm eliminates the deficiency of exiting k mean. It first calculates the initial centro
requirements of users and then gives better, effective and stable cluster. It also takes less execution time because it eliminates
unnecessary distance computation by using previous iteration. The new approach for k
systematically based on initial centroids. It generates stable clusters to improve accuracy.
An improvement in k mean clustering algorithm using better time and accuracyijpla
This document summarizes a research paper that proposes an improved K-means clustering algorithm to enhance accuracy and reduce computation time. The standard K-means algorithm randomly selects initial cluster centroids, affecting results. The proposed algorithm systematically determines initial centroids based on data point distances. It assigns data to the closest initial centroid to generate initial clusters. Iteratively, it calculates new centroids and reassigns data only if distances decrease, reducing unnecessary computations. Experiments on various datasets show the proposed algorithm achieves higher accuracy faster than standard K-means.
K-MEDOIDS CLUSTERING USING PARTITIONING AROUND MEDOIDS FOR PERFORMING FACE R...ijscmc
Face recognition is one of the most unobtrusive biometric techniques that can be used for access control as well as surveillance purposes. Various methods for implementing face recognition have been proposed with varying degrees of performance in different scenarios. The most common issue with effective facial biometric systems is high susceptibility of variations in the face owing to different factors like changes in pose, varying illumination, different expression, presence of outliers, noise etc. This paper explores a novel technique for face recognition by performing classification of the face images using unsupervised learning approach through K-Medoids clustering. Partitioning Around Medoids algorithm (PAM) has been used for performing K-Medoids clustering of the data. The results are suggestive of increased robustness to noise and outliers in comparison to other clustering methods. Therefore the technique can also be used to increase the overall robustness of a face recognition system and thereby increase its invariance and make it a reliably usable biometric modality
The comparison study of kernel KC-means and support vector machines for class...TELKOMNIKA JOURNAL
Schizophrenia is one of mental disorder that affects the mind, feeling, and behavior. Its treatment is usually permanent and quite complicated; therefore, early detection is important. Kernel KC-means and support vector machines are the methods known as a good classifier. This research, therefore, aims to compare kernel KC-means and support vector machines, using data obtained from Northwestern University, which consists of 171 schizophrenia and 221 non-schizophrenia samples. The performance accuracy, F1-score, and running time were examined using the 10-fold cross-validation method. From the experiments, kernel KC-means with the sixth-order polynomial kernel gives 87.18 percent accuracy and 93.15 percent F1-score at the faster running time than support vector machines. However, with the same kernel, it was further deduced from the results that support vector machines provides better performance with an accuracy of 88.78 percent and F1-score of 94.05 percent.
Optimising Data Using K-Means Clustering AlgorithmIJERA Editor
K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each cluster. These centroids should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each other.
This document provides an overview of clustering techniques. It defines clustering as grouping a set of similar objects into classes, with objects within a cluster being similar to each other and dissimilar to objects in other clusters. The document then discusses partitioning, hierarchical, and density-based clustering methods. It also covers mathematical elements of clustering like partitions, distances, and data types. The goal of clustering is to minimize a similarity function to create high similarity within clusters and low similarity between clusters.
This document outlines clustering algorithms for large datasets. It discusses k-means clustering and extensions like k-means++ that improve initialization. It also covers spectral relaxation methods that reformulate k-means as a trace maximization problem to address local minima. Additionally, it proposes landmark-based clustering algorithms for biological sequences that select landmarks in one pass and assign sequences to the nearest landmark using hashing to search for neighbors. The document provides analysis of the time and space complexity of these algorithms as well as assumptions about separability and cluster size.
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
Cluster analysis involves grouping data objects into clusters so that objects within the same cluster are more similar to each other than objects in other clusters. There are several major clustering approaches including partitioning methods that iteratively construct partitions, hierarchical methods that create hierarchical decompositions, density-based methods based on connectivity and density, grid-based methods using a multi-level granularity structure, and model-based methods that find the best fit of a model to the clusters. Partitioning methods like k-means and k-medoids aim to optimize a partitioning criterion by iteratively updating cluster centroids or medoids.
Pattern recognition binoy k means clustering108kaushik
This document discusses clustering and the k-means clustering algorithm. It defines clustering as grouping a set of data objects into clusters so that objects within the same cluster are similar to each other but dissimilar to objects in other clusters. The k-means algorithm is described as an iterative process that assigns each object to one of k predefined clusters based on the object's distance from the cluster's centroid, then recalculates the centroid, repeating until cluster assignments no longer change. A worked example demonstrates how k-means partitions 7 objects into 2 clusters over 3 iterations. The k-means algorithm is noted to be efficient but requires specifying k and can be impacted by outliers, noise, and non-convex cluster shapes.
This document presents an exponential-Lindley additive failure rate model (ELAFRM) by combining the hazard functions of an exponential distribution and a Lindley distribution. The key properties of the ELAFRM are derived, including the probability density function, cumulative distribution function, hazard function, moments, and graphical representations. Estimation of the model parameters is also discussed. The document proposes this new ELAFRM distribution and analyzes its mathematical properties.
Step by step operations by which we make a group of objects in which attributes
of all the objects are nearly similar, known as clustering. So, a cluster is a collection of
objects that acquire nearly same attribute values. The property of an object in a cluster is
similar to other objects in same cluster but different with objects of other clusters.
Clustering is used in wide range of applications like pattern recognition, image processing,
data analysis, machine learning etc. Nowadays, more attention has been put on categorical
data rather than numerical data. Where, the range of numerical attributes organizes in a
class like small, medium, high, and so on. There is wide range of algorithm that used to
make clusters of given categorical data. Our approach is to enhance the working on well-
known clustering algorithm k-modes to improve accuracy of algorithm. We proposed a new
approach named “High Accuracy Clustering Algorithm for Categorical datasets”.
Rainfall Prediction using Data-Core Based Fuzzy Min-Max Neural Network for Cl...IJERA Editor
This paper proposes the Rainfall Prediction System by using classification technique. The advanced and modified neural network called Data Core Based Fuzzy Min Max Neural Network (DCFMNN) is used for pattern classification. This classification method is applied to predict Rainfall. The neural network called fuzzy min max neural network (FMNN) that creates hyperboxes for classification and predication, has a problem of overlapping neurons that resoled in DCFMNN to give greater accuracy. This system is composed of forming of hyperboxes, and two kinds of neurons called as Overlapping Neurons and Classifying neurons, and classification used for prediction. For each kind of hyperbox its data core and geometric center of data is calculated. The advantage of this method is it gives high accuracy and strong robustness. According to evaluation results we can say that this system gives better prediction of rainfall and classification tool in real environment.
EXPERIMENTS ON HYPOTHESIS "FUZZY K-MEANS IS BETTER THAN K-MEANS FOR CLUSTERING"IJDKP
Clustering is one of the data mining techniques that have been around to discover business intelligence by grouping objects into clusters using a similarity measure. Clustering is an unsupervised learning process that has many utilities in real time applications in the fields of marketing, biology, libraries, insurance, city-planning, earthquake studies and document clustering. Latent trends and relationships among data objects can be unearthed using clustering algorithms. Many clustering algorithms came into existence. However, the quality of clusters has to be given paramount importance. The quality objective is to achieve
highest similarity between objects of same cluster and lowest similarity between objects of different clusters. In this context, we studied two widely used clustering algorithms such as the K-Means and Fuzzy K-Means. K-Means is an exclusive clustering algorithm while the Fuzzy K-Means is an overlapping clustering algorithm. In this paper we prove the hypothesis “Fuzzy K-Means is better than K-Means for Clustering” through both literature and empirical study. We built a prototype application to demonstrate the differences between the two clustering algorithms. The experiments are made on diabetes dataset
obtained from the UCI repository. The empirical results reveal that the performance of Fuzzy K-Means is better than that of K-means in terms of quality or accuracy of clusters. Thus, our empirical study proved the hypothesis “Fuzzy K-Means is better than K-Means for Clustering”.
Hierarchical clustering is a method of partitioning a set of data into meaningful sub-classes or clusters. It involves two approaches - agglomerative, which successively links pairs of items or clusters, and divisive, which starts with the whole set as a cluster and divides it into smaller partitions. Agglomerative Nesting (AGNES) is an agglomerative technique that merges clusters with the least dissimilarity at each step, eventually combining all clusters. Divisive Analysis (DIANA) is the inverse, starting with all data in one cluster and splitting it until each data point is its own cluster. Both approaches can be visualized using dendrograms to show the hierarchical merging or splitting of clusters.
Mandar Valmik Jadhav is seeking a responsible and challenging position where he can enhance his skills and contribute to organizational success. He has a Bachelor of Engineering degree in Mechanical Engineering from Mumbai University and Leelavati Awhad Institute of Technology with 58.6% aggregate marks until the 7th semester. He has participated in technical quiz competitions and volunteered in sports tournaments. Mandar also attended workshops on automobiles and seminars on related topics. He was born in 1993 and resides in Mumbai.
The document provides an overview of cluster analysis techniques. It discusses the need for segmentation to group large populations into meaningful subsets. Common clustering algorithms like k-means are introduced, which assign data points to clusters based on similarity. The document also covers calculating distances between observations, defining the distance between clusters, and interpreting the results of clustering analysis. Real-world applications of segmentation and clustering are mentioned such as market research, credit risk analysis, and operations management.
K-Means clustering uses an iterative procedure which is very much sensitive and dependent upon the initial centroids. The initial centroids in the k-means clustering are chosen randomly, and hence the clustering also changes with respect to the initial centroids. This paper tries to overcome this problem of random selection of centroids and hence change of clusters with a premeditated selection of initial centroids. We have used the iris, abalone and wine data sets to demonstrate that the proposed method of finding the initial centroids and using the centroids in k-means algorithm improves the clustering performance. The clustering also remains the same in every run as the initial centroids are not randomly selected but through premeditated method.
Hybridization of Bat and Genetic Algorithm to Solve N-Queens ProblemjournalBEEI
In this paper, a hybrid of Bat-Inspired Algorithm (BA) and Genetic Algorithm (GA) is proposed to solve N-queens problem. The proposed algorithm executes the behavior of microbats with changing pulse rates of emissions and loudness to final all the possible solutions in the initialization and moving phases. This dataset applied two metaheuristic algorithms (BA and GA) and the hybrid to solve N-queens problem by finding all the possible solutions in the instance with the input sizes of area 8*8, 20*20, 50*50, 100*100 and 500*500 on a chessboard. To find the optimal solution, consistently, ten run have been set with 100 iterations for all the input sizes. The hybrid algorithm obtained substantially better results than BA and GA because both algorithms were inferior in discovering the optimal solutions than the proposed randomization method. It also has been discovered that BA outperformed GA because it requires a reduced amount of steps in determining the solutions.
This document discusses different approaches to multivariate data analysis and clustering, including nearest neighbor methods, hierarchical clustering, and k-means clustering. It provides examples of using Ward's method, average linkage, and k-means clustering on poverty data to identify potential clusters of countries based on variables like birth rate, death rate, and infant mortality rate. Key lessons are that different linkage methods, distance measures, and data normalizations should be tested and that higher-dimensional data may require different variable spaces or transformations to identify meaningful clusters.
K-Means, its Variants and its ApplicationsVarad Meru
This presentation was given by our project group at the Lead College competition at Shivaji University. Our project got the 1st Prize. We focused mainly on Rough K-Means and build a Social-Network-Recommender System based on Rough K-Means.
The Members of the Project group were -
Mansi Kulkarni,
Nikhil Ingole,
Prasad Mohite,
Varad Meru
Vishal Bhavsar.
Wonderful Experience !!!
New Approach for K-mean and K-medoids AlgorithmEditor IJCATR
K-means and K-medoids clustering algorithms are widely used for many practical applications. Original k
medoids algorithms select initial centroids and medoids randomly that affect the quality of the resulting clusters and sometimes it
generates unstable and empty clusters which are meaningless.
expensive and requires time proportional to the product of the number of data items, number of clusters and the number of iterations.
The new approach for the k mean algorithm eliminates the deficiency of exiting k mean. It first calculates the initial centro
requirements of users and then gives better, effective and stable cluster. It also takes less execution time because it eliminates
unnecessary distance computation by using previous iteration. The new approach for k
systematically based on initial centroids. It generates stable clusters to improve accuracy.
An improvement in k mean clustering algorithm using better time and accuracyijpla
This document summarizes a research paper that proposes an improved K-means clustering algorithm to enhance accuracy and reduce computation time. The standard K-means algorithm randomly selects initial cluster centroids, affecting results. The proposed algorithm systematically determines initial centroids based on data point distances. It assigns data to the closest initial centroid to generate initial clusters. Iteratively, it calculates new centroids and reassigns data only if distances decrease, reducing unnecessary computations. Experiments on various datasets show the proposed algorithm achieves higher accuracy faster than standard K-means.
K-MEDOIDS CLUSTERING USING PARTITIONING AROUND MEDOIDS FOR PERFORMING FACE R...ijscmc
Face recognition is one of the most unobtrusive biometric techniques that can be used for access control as well as surveillance purposes. Various methods for implementing face recognition have been proposed with varying degrees of performance in different scenarios. The most common issue with effective facial biometric systems is high susceptibility of variations in the face owing to different factors like changes in pose, varying illumination, different expression, presence of outliers, noise etc. This paper explores a novel technique for face recognition by performing classification of the face images using unsupervised learning approach through K-Medoids clustering. Partitioning Around Medoids algorithm (PAM) has been used for performing K-Medoids clustering of the data. The results are suggestive of increased robustness to noise and outliers in comparison to other clustering methods. Therefore the technique can also be used to increase the overall robustness of a face recognition system and thereby increase its invariance and make it a reliably usable biometric modality
The comparison study of kernel KC-means and support vector machines for class...TELKOMNIKA JOURNAL
Schizophrenia is one of mental disorder that affects the mind, feeling, and behavior. Its treatment is usually permanent and quite complicated; therefore, early detection is important. Kernel KC-means and support vector machines are the methods known as a good classifier. This research, therefore, aims to compare kernel KC-means and support vector machines, using data obtained from Northwestern University, which consists of 171 schizophrenia and 221 non-schizophrenia samples. The performance accuracy, F1-score, and running time were examined using the 10-fold cross-validation method. From the experiments, kernel KC-means with the sixth-order polynomial kernel gives 87.18 percent accuracy and 93.15 percent F1-score at the faster running time than support vector machines. However, with the same kernel, it was further deduced from the results that support vector machines provides better performance with an accuracy of 88.78 percent and F1-score of 94.05 percent.
Optimising Data Using K-Means Clustering AlgorithmIJERA Editor
K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each cluster. These centroids should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each other.
This document provides an overview of clustering techniques. It defines clustering as grouping a set of similar objects into classes, with objects within a cluster being similar to each other and dissimilar to objects in other clusters. The document then discusses partitioning, hierarchical, and density-based clustering methods. It also covers mathematical elements of clustering like partitions, distances, and data types. The goal of clustering is to minimize a similarity function to create high similarity within clusters and low similarity between clusters.
This document outlines clustering algorithms for large datasets. It discusses k-means clustering and extensions like k-means++ that improve initialization. It also covers spectral relaxation methods that reformulate k-means as a trace maximization problem to address local minima. Additionally, it proposes landmark-based clustering algorithms for biological sequences that select landmarks in one pass and assign sequences to the nearest landmark using hashing to search for neighbors. The document provides analysis of the time and space complexity of these algorithms as well as assumptions about separability and cluster size.
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
Cluster analysis involves grouping data objects into clusters so that objects within the same cluster are more similar to each other than objects in other clusters. There are several major clustering approaches including partitioning methods that iteratively construct partitions, hierarchical methods that create hierarchical decompositions, density-based methods based on connectivity and density, grid-based methods using a multi-level granularity structure, and model-based methods that find the best fit of a model to the clusters. Partitioning methods like k-means and k-medoids aim to optimize a partitioning criterion by iteratively updating cluster centroids or medoids.
Pattern recognition binoy k means clustering108kaushik
This document discusses clustering and the k-means clustering algorithm. It defines clustering as grouping a set of data objects into clusters so that objects within the same cluster are similar to each other but dissimilar to objects in other clusters. The k-means algorithm is described as an iterative process that assigns each object to one of k predefined clusters based on the object's distance from the cluster's centroid, then recalculates the centroid, repeating until cluster assignments no longer change. A worked example demonstrates how k-means partitions 7 objects into 2 clusters over 3 iterations. The k-means algorithm is noted to be efficient but requires specifying k and can be impacted by outliers, noise, and non-convex cluster shapes.
This document presents an exponential-Lindley additive failure rate model (ELAFRM) by combining the hazard functions of an exponential distribution and a Lindley distribution. The key properties of the ELAFRM are derived, including the probability density function, cumulative distribution function, hazard function, moments, and graphical representations. Estimation of the model parameters is also discussed. The document proposes this new ELAFRM distribution and analyzes its mathematical properties.
Step by step operations by which we make a group of objects in which attributes
of all the objects are nearly similar, known as clustering. So, a cluster is a collection of
objects that acquire nearly same attribute values. The property of an object in a cluster is
similar to other objects in same cluster but different with objects of other clusters.
Clustering is used in wide range of applications like pattern recognition, image processing,
data analysis, machine learning etc. Nowadays, more attention has been put on categorical
data rather than numerical data. Where, the range of numerical attributes organizes in a
class like small, medium, high, and so on. There is wide range of algorithm that used to
make clusters of given categorical data. Our approach is to enhance the working on well-
known clustering algorithm k-modes to improve accuracy of algorithm. We proposed a new
approach named “High Accuracy Clustering Algorithm for Categorical datasets”.
Rainfall Prediction using Data-Core Based Fuzzy Min-Max Neural Network for Cl...IJERA Editor
This paper proposes the Rainfall Prediction System by using classification technique. The advanced and modified neural network called Data Core Based Fuzzy Min Max Neural Network (DCFMNN) is used for pattern classification. This classification method is applied to predict Rainfall. The neural network called fuzzy min max neural network (FMNN) that creates hyperboxes for classification and predication, has a problem of overlapping neurons that resoled in DCFMNN to give greater accuracy. This system is composed of forming of hyperboxes, and two kinds of neurons called as Overlapping Neurons and Classifying neurons, and classification used for prediction. For each kind of hyperbox its data core and geometric center of data is calculated. The advantage of this method is it gives high accuracy and strong robustness. According to evaluation results we can say that this system gives better prediction of rainfall and classification tool in real environment.
EXPERIMENTS ON HYPOTHESIS "FUZZY K-MEANS IS BETTER THAN K-MEANS FOR CLUSTERING"IJDKP
Clustering is one of the data mining techniques that have been around to discover business intelligence by grouping objects into clusters using a similarity measure. Clustering is an unsupervised learning process that has many utilities in real time applications in the fields of marketing, biology, libraries, insurance, city-planning, earthquake studies and document clustering. Latent trends and relationships among data objects can be unearthed using clustering algorithms. Many clustering algorithms came into existence. However, the quality of clusters has to be given paramount importance. The quality objective is to achieve
highest similarity between objects of same cluster and lowest similarity between objects of different clusters. In this context, we studied two widely used clustering algorithms such as the K-Means and Fuzzy K-Means. K-Means is an exclusive clustering algorithm while the Fuzzy K-Means is an overlapping clustering algorithm. In this paper we prove the hypothesis “Fuzzy K-Means is better than K-Means for Clustering” through both literature and empirical study. We built a prototype application to demonstrate the differences between the two clustering algorithms. The experiments are made on diabetes dataset
obtained from the UCI repository. The empirical results reveal that the performance of Fuzzy K-Means is better than that of K-means in terms of quality or accuracy of clusters. Thus, our empirical study proved the hypothesis “Fuzzy K-Means is better than K-Means for Clustering”.
Hierarchical clustering is a method of partitioning a set of data into meaningful sub-classes or clusters. It involves two approaches - agglomerative, which successively links pairs of items or clusters, and divisive, which starts with the whole set as a cluster and divides it into smaller partitions. Agglomerative Nesting (AGNES) is an agglomerative technique that merges clusters with the least dissimilarity at each step, eventually combining all clusters. Divisive Analysis (DIANA) is the inverse, starting with all data in one cluster and splitting it until each data point is its own cluster. Both approaches can be visualized using dendrograms to show the hierarchical merging or splitting of clusters.
Mandar Valmik Jadhav is seeking a responsible and challenging position where he can enhance his skills and contribute to organizational success. He has a Bachelor of Engineering degree in Mechanical Engineering from Mumbai University and Leelavati Awhad Institute of Technology with 58.6% aggregate marks until the 7th semester. He has participated in technical quiz competitions and volunteered in sports tournaments. Mandar also attended workshops on automobiles and seminars on related topics. He was born in 1993 and resides in Mumbai.
Allele Frequencies as Stochastic Processes: Mathematical & Statistical Approa...Gota Morota
The document discusses modeling allele frequency changes over time as stochastic processes. It describes allele frequencies changing as random walks or Brownian motion. It presents the Fokker-Planck equation for describing the probability distribution of allele frequencies over time under various evolutionary forces like genetic drift, selection, and mutation. The steady state distribution of allele frequencies and solutions to the Fokker-Planck equation are discussed for different evolutionary scenarios. Time series analysis methods are introduced for modeling allele frequency change as a discrete time process. An example application to cattle genotype data is shown.
Prateek Sharma is seeking a job and has provided his resume. He has completed a B.Tech in computer science from Echelon Institute of Technology in 2012. He has 6 years of experience as a software engineer and android developer working on various mobile applications. His most recent role is as a senior android developer at Mag Studios. He provides details of his past projects and roles, education qualifications, skills, and personal details in his resume.
Diffusion kernels on SNP data embedded in a non-Euclidean metricGota Morota
This document discusses using diffusion kernels on single nucleotide polymorphism (SNP) data embedded in non-Euclidean metric spaces. It defines kernels as weighting functions that provide a similarity metric based on a chosen distance metric, such as Euclidean or Manhattan distance. The document explores using graphs to represent SNP data, with nodes for each genotype and edges connecting similar genotypes. It describes constructing diffusion kernels on these graphs by taking Cartesian graph products of one-dimensional genotype graphs. This allows modeling SNP data as grids where similarity decreases as the number of differing loci increases.
Application of Bayesian and Sparse Network Models for Assessing Linkage Diseq...Gota Morota
The document summarizes Gota Morota's master's thesis defense on applying Bayesian and sparse network models to assess linkage disequilibrium in animals and plants. The thesis aims to evaluate linkage disequilibrium (LD) using networks that capture loci associations. It first provides background on standard LD metrics and graphical models. It then describes using a Bayesian network and L1-regularized Markov network to analyze LD in dairy cattle, identifying networks of strongly associated SNPs related to milk protein yield. The thesis concludes the results support LD having a multivariate nature better described by networks than pairwise metrics alone.
Application of Bayesian and Sparse Network Models for Assessing Linkage Diseq...Gota Morota
This document describes using Bayesian network and sparse network models to assess linkage disequilibrium in animals and plants. It introduces the Incremental Association Markov Blanket algorithm to learn the network structure and identifies each node's Markov blanket. Pairwise linkage disequilibrium is estimated using r^2, while sparse pairwise binary Markov networks estimate associations conditioned on other variables using L1 regularization. The approach is demonstrated on SNP data from Holstein bulls and wheat lines, identifying the top associations between SNPs for milk protein yield and grain yield, respectively.
Learn Youtube marketing and make your carrier in youtubeNayeem Talukder
How to success in youtube marketing easy. In my Youtube Marketing Tutorial Share some More important topic and idea every unique idea helps to success in video marketing.
Also learn More Visite my website http://www.nayeemtalukder.com/ and www.onlineteachingbd.com for online teaching.
The document discusses the results of a study on the effects of a new drug on memory and cognitive function in older adults. The double-blind study involved 100 participants aged 65-80 who were given either the drug or a placebo daily for 6 months. Researchers found that those who received the drug performed significantly better on memory and problem-solving tests at the end of the study compared to those who received the placebo.
The excellence products of Pure Made in Italy.
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The excellence of Made in Italy is linked to territories. It is the result of small-medium enterprises that excel in the production and style.
The products they make speak of their creators, of the territory where it lays Italy‘ most pure essence. An essence made of the thousand details that make a difference. Layers on layers of stories, emotions, rituals never repeating themselves.
Limited Edition
Due to artisanal products, limited production, means selectiveness and exclusiveness
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You will have access to the creativity and innovation of artisanal Made in Italy and it's passion for beauty and quality. You can choose garments & accessories, that will be unique to your taste and style.
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The Ethical approach
We are addressing a "responsible consumer". You, increasingly aware of the history and characteristics of the product, who are looking for garments & accessories that are an expression of a match between high quality handcrafts and manufacturing ethic's.
Explore more about the Brazil country and help people know about your business, use excellent map templates. It is the largest Arab state in western Asia that is known for its seamless beauty and large skyscrapers. Visit for more: http://www.powerpointmapsonline.com/powerpointmaps.aspx/Geographical-Map-Of-Saudi-Arabia-32
Clustering and Visualisation using R programmingNixon Mendez
Clustering Analysis is a collection of patterns into clusters based on similarity.
Here we will discuss on the following :
Microarray Data of Yeast Cell Cycle
Clustering Analysis :-
Principal Component Analysis (PCA)
Multidimensional Scaling (MDS)
K-Means
Self-Organizing Maps (SOM)
Hierarchical Clustering
- The document analyzes the Kaggle Digits dataset using linear discriminant analysis (LDA) to classify handwritten digits
- LDA accuracy was evaluated using repeated cross-validation as the number of components was increased from 10 to 100
- The optimal LDA model used 100 components and achieved an accuracy of 87.6% on the validation data
- This model was then used to predict labels on the test set, achieving similar accuracy percentages for each digit class
This document discusses techniques for fast decision tree learning on microarray data. It introduces using attribute histograms to speed up the process of finding the best split points for decision tree learning. It also discusses optimizations for speeding up leave-one-out cross validation by reusing subtrees from previous runs. Experimental results on three microarray datasets show speedups of 150-400% from these techniques. Attribute pruning based on histogram indices is also introduced to further improve speed without loss of accuracy.
The document discusses different clustering methods in R including k-means clustering, k-medoids clustering, hierarchical clustering, and density-based clustering. It provides code examples to demonstrate each method using the iris dataset. For k-means and k-medoids clustering, it shows how to interpret the results and check clustering against known classes. For hierarchical clustering, it generates a dendrogram and identifies clusters. For density-based clustering, it identifies clusters of different shapes and sizes and is able to label new prediction data.
CCC-Bicluster Analysis for Time Series Gene Expression DataIRJET Journal
The document presents a CCC-Biclustering (Contiguous Column Coherence) algorithm for identifying biclusters in time series gene expression data. The algorithm finds maximal biclusters with adjacent/contiguous columns in linear time using Ukkonen's suffix tree construction algorithm and discretized gene expression matrices. The algorithm was applied to a Saccharomyces cerevisiae gene expression time series in response to heat stress. It identifies coherent expression patterns shared among genes over contiguous time points, potentially revealing relevant regulatory modules.
Statistical Data Analysis on a Data Set (Diabetes 130-US hospitals for years ...Seval Çapraz
This document analyzes a dataset of diabetes records from 130 US hospitals from 1999-2008 using various statistical data analysis and machine learning techniques. It first performs dimensionality reduction using principal component analysis (PCA) and multidimensional scaling (MDS). It then clusters the data using hierarchical clustering and k-means clustering. Cluster validity is assessed using precision. Spectral clustering is also applied and validated using Dunn and Davies-Bouldin indexes, with complete linkage diameter performing best.
1. The document discusses unsupervised machine learning techniques for classification including cluster seeking algorithms like k-means and maximin as well as cluster refinement algorithms.
2. It provides examples of using the k-means algorithm to determine tentative clusters in a 2D feature space by calculating distances between data points and cluster centers.
3. The k-means algorithm is then shown refining the initial cluster centers through iterative reassignment of data points to clusters and recalculation of cluster centers until cluster membership stabilizes.
My name is Rose Tom. I am associated with statisticsassignmenthelp.com for the past 8 years and have been helping statistics students with their MyStataLab assignments.
I have a master's in Professional Statistics from Princeton University, USA.
ENHANCED BREAST CANCER RECOGNITION BASED ON ROTATION FOREST FEATURE SELECTIO...cscpconf
Optimization problems are dominantly being solved using Computational Intelligence. One of
the issues that can be addressed in this context is problems related to attribute subset selection
evaluation. This paper presents a computational intelligence technique for solving the
optimization problem using a proposed model called Modified Genetic Search Algorithms
(MGSA) that avoids local bad search space with merit and scaled fitness variables, detecting
and deleting bad candidate chromosomes, thereby reducing the number of individual
chromosomes from search space and subsequent iterations in next generations. This paper aims
to show that Rotation forest ensembles are useful in the feature selection method. The base
classifier is multinomial logistic regression method integrated with Haar wavelets as projection
filter and reproducing the ranks of each features with 10 fold cross validation method. It also
discusses the main findings and concludes with promising result of the proposed model. It
explores the combination of MGSA for optimization with Naïve Bayes classification. The result
obtained using proposed model MGSA is validated mathematically using Principal Component
Analysis. The goal is to improve the accuracy and quality of diagnosis of Breast cancer disease
with robust machine learning algorithms. As compared to other works in literature survey,
experimental results achieved in this paper show better results with statistical inferenc
This document provides an overview of unsupervised learning and clustering algorithms. It discusses the motivation for clustering as grouping similar data points without labels. It introduces common clustering algorithms like K-means, hierarchical clustering, and fuzzy C-means. It covers clustering criteria such as similarity functions, stopping criteria, and cluster quality. It also discusses techniques like data normalization and challenges in evaluating clusters without ground truths. The document aims to explain the concepts and applications of unsupervised learning for clustering unlabeled data.
This document provides an overview of supervised and unsupervised learning, with a focus on clustering as an unsupervised learning technique. It describes the basic concepts of clustering, including how clustering groups similar data points together without labeled categories. It then covers two main clustering algorithms - k-means, a partitional clustering method, and hierarchical clustering. It discusses aspects like cluster representation, distance functions, strengths and weaknesses of different approaches. The document aims to introduce clustering and compare it with supervised learning.
Fuzzy clustering and fuzzy c-means partition cluster analysis and validation ...IJECEIAES
A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value < 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter m was evaluated to determine the distribution of membership values with variation in m from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters.
This document provides an overview of machine learning techniques that can be applied in finance, including exploratory data analysis, clustering, classification, and regression methods. It discusses statistical learning approaches like data mining and modeling. For clustering, it describes techniques like k-means clustering, hierarchical clustering, Gaussian mixture models, and self-organizing maps. For classification, it mentions discriminant analysis, decision trees, neural networks, and support vector machines. It also provides summaries of regression, ensemble methods, and working with big data and distributed learning.
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...IJRES Journal
The document presents a mathematical programming approach for selecting important variables in cluster analysis. It formulates a nonlinear binary model to minimize the distance between observations within clusters, using indicator variables to select important variables. The model is applied to a sample dataset of 30 observations across 5 variables, correctly identifying variables 3, 4 and 5 as most important for clustering the observations into two groups. The results are compared to an existing variable selection heuristic, with the mathematical programming approach achieving a 100% correct classification versus 97% for the other method.
- Maximum likelihood attempts to find the phylogenetic tree and evolutionary model that have the highest probability of producing the observed sequence data.
- The likelihood of observing the data depends on the evolutionary model used and can change if the model changes, even if the data remains the same.
- Markov chain Monte Carlo methods are used in Bayesian inference to approximate the posterior probabilities of trees since the exact joint probabilities cannot be calculated analytically. Trees are sampled from their posterior probability distribution to make inferences.
- Maximum likelihood attempts to find the phylogenetic tree and evolutionary model that have the highest probability of producing the observed sequence data.
- The likelihood of observing the data depends on the evolutionary model used to generate the sequences. More complex models with more parameters will generally fit the data better but can also overfit.
- Bayesian inference finds the tree topology and parameters that have the highest posterior probability given the data, using Markov chain Monte Carlo sampling to approximate the posterior probabilities when they cannot be calculated directly.
- Maximum likelihood attempts to find the phylogenetic tree and evolutionary model that have the highest probability of producing the observed sequence data.
- The likelihood of observing the data depends on the evolutionary model used and can change if the model changes, even if the data remains the same.
- Markov chain Monte Carlo methods are used in Bayesian inference to approximate the posterior probabilities of trees since the exact joint probabilities cannot be calculated analytically. Trees are sampled from their posterior probability distribution to make inferences.
- Maximum likelihood attempts to find the phylogenetic tree and evolutionary model that have the highest probability of producing the observed sequence data.
- The likelihood of observing the data depends on the evolutionary model used and can change if the model changes, even if the data remains the same.
- Markov chain Monte Carlo methods are used in Bayesian inference to approximate the posterior probabilities of trees since the exact joint probabilities cannot be calculated analytically. Trees are sampled from their posterior probability distribution to make inferences.
Similar to Garge, Nikhil et. al. 2005. Reproducible Clusters from Microarray Research: Whither? BMC Bioinformatics. (20)
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
2. DYS875-006
Seminar
Clustering Gene Expression Profiles
Given: expression profiles for a set of genes or
experiments/individuals/time points
Do: organize profiles into clusters such that
genes in the same cluster are highly similar to each other
genes from different clusters have low similarity to each other
Goal
Understand general characteristics of data and infer something
about a gene based on how it relates to other genes
Gota Morota
Reproducible Clusters from Microarray Research: Whither?
3. Seminar
DYS875-006
Validity of Clustering Analysis
Clustering presents challenges because
there is no null hypothesis to test and no right answer
the result of clustering may be method sensitive (distance
metric, clustering algorithm)
no way to evaluate the validity of a cluster solution
⇓
Measure replicability of clustering algorithms.
Clusters that produce classifications with greater replicability
would be considered more valid.
Objective
Determine the replicability and degree of stability of commonly
used non-hierarchical clustering algorithms
Gota Morota
Reproducible Clusters from Microarray Research: Whither?
4. DYS875-006
Seminar
Data
!"#$!%&%'(&)*+,%-.!"##$%!!&'())*!"+,'-#
Real datasets
Simulated datasets
!"#$!%&%'(&)*+,%-.!"##$%!!&'())*!"+,'-#
Table 1: List of microarray datasets considered for the study. Table 1 contains two columns of datasets. Each dataset is described by its
name, source, and sample size (n). Table 1 shows 39 datasets. The first 3 columns list 19 datasets and last three columns describe 18
datasets.
Name of the
dataset
Source
Sample size (n)
Name of the dataset
Source
Sample size (n)
GDS22
GDS171
GDS184
GDS232
GDS274
GDS285
GDS365
GEO
GEO
GEO
GEO
GEO
GEO
GEO
80
30
30
46
80
20
66
[30]
[31]
[32]
[33]
[34]
Unpublished
Unpublished
70
34
100
60
42
24
106
GDS465
GDS331
GDS534
GDS565
GDS427
GDS402
GDS356
GDS389
GDS388
GDS352
GDS531
GDS535
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
90
70
74
48
24
12
14
16
18
12
172
12
Leukemia dataset
Medulloblastoma Data Set
Prostate Cancer dataset
Gaffney Head and Neck data
Affymetrix Hu133A Latin Square
CNGI design experiment
Paired pre and post euglycaemic insulin clamp skeletal
muscle biopsies
GDS156
GDS254
GDS268
GDS287
GDS288
GDS472
GDS473
GDS511
GDS520
GDS564
GDS540
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
GEO
12
16
24
16
16
14
12
12
20
28
18
Table 2: List of simulated microarray datasets. Table 2 show the
details of simulated datasets. Each of these datasets has
clustering structure k = 6 (six clusters) with correlation ! set to
(0.33)1/2.
3. Compute th
not updated un
the data.
Dataset Name Sample size Number of genes Clusters
4. Alternate ste
ters.
Dataset1
Dataset2
Dataset3
Dataset4
Dataset5
Dataset6
Dataset7
Dataset8
20
100
200
500
1000
40
60
80
1200
1200
1200
1200
1200
1200
1200
1200
6
6
6
6
6
6
6
6
We consider
methods, whic
algorithms for
package.
K-means
In K-means clu
ters and rando
Filtered out genes values
which contained at least one missing value ters. If a gene
Missing
not available in real datasets. We simulated 8 datasets
If we represent microarray data as a matrix with rows repwith 1200 genes and sample sizes ranging from n = 20 to
cluster, as asse
resenting genes and columns representing chips or
1000, where n is the number of subjects. All simulated
Igij !
Standardized the we filtered out all rows which contained at leastsam- meanIgizero and unit
ples, expression values to
one
datasets were structured for 6 clusters (k = 6) with correlaPearson's corre
Zij =
null expression or missing value because we do not know
tion ! set to (0.33) for all pairwise combinations of
SDgi
genes within clusters and zero for all pair wise combinasource(s) for the
observation.
be assigned to
variance to validate our the exactdata can be due missing/null valuetranscription
tions of genes in different clusters. In order
Missing
to array damage,
to the closest cl
methodology, we would predict higher scores when we
errors, etc. Conventional algorithms for clustering require
extract 6 clusters in our fitted solutions. Simulated datacomplete datasets to run and extending these clustering
Where Zij = Z score computed for expression level recalculated. A
sets also help us understand the stability behaviour for
routines to accommodate missing data was beyond the
values other than k = 6 (i.e., when we extract the wrong
scope of our inquiry.
troids will no l
number of clusters). Table 2 explains the details of simuobserved for gene i in sample/subject j, Ig = intensity
Standardization
lated datasets. We acknowledge that number of genes in
Gota Morota
Reproducible Clusters from Microarray Research:ij Whither? K-means cluste
1/2
5. DYS875-006
Seminar
Four Algorithms Considered
Four non-hierarchical (partitional) clustering algorithms.
Non-hierarchical clusterings require the number of clusters (k) be
pre-specified.
K-means ( kmeans {stats} )
Self Organizing Maps (SOM) (som { cluster })
Clustering LARge Applications (CLARA) (clara { cluster })
Fuzzy C-means (fanny { cluster })
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Reproducible Clusters from Microarray Research: Whither?
6. Seminar
DYS875-006
K-means
1
2
3
4
5
6
K-Means Clustering
Choose the number of k clusters
Randomly assign items to the k clusters
•! assume our instances are represented by vectors of real
Calculate new centroid for each of the k clusters
values
Calculate the distance of all items to the k centroids
•! put k cluster centers in same space as instances
!
Assign items to closest centroid
•! each cluster is represented by a vector f j
Repeat until clusters assignments are stable
•! consider an example in which our vectors have 2 dimensions
+
instances
+
+
cluster center
+
Figure 1: K-Means
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Reproducible Clusters from Microarray Research: Whither?
7. DYS875-006
Seminar
K-means
K-Means Clustering
Each iteration involves two steps
1
•! each iteration involves two steps
assignment of instances to clusters
2
–! assignment the means
re-computation of of instances to clusters
–! re-computation of the means
+
+
+
+
+
+
+
+
assignment
re-computation of means
Figure 2: K-Means
Gota Morota
Reproducible Clusters from Microarray Research: Whither?
8. DYS875-006
Seminar
Other Clustering Methods
Self Organazing Map (SOM)
Similar to K-means, but centroids are restricted to a
two-dimensional grid
Clustering LARge Applications (CLARA)
Extension of PAM(Partition Around Medoids)
it can deal with much larger datasets than PAM
Fuzzy C-means
each gene belongs to a cluster that is specified by a
membership degree (0-1)
basically you can assign genes to more than one cluster
assign the gene to a cluster showing maximum degree of
membership
Gota Morota
Reproducible Clusters from Microarray Research: Whither?
9. DYS875-006
Seminar
Cluster Stability
Cramer’s v2
χ2
N(k − 1)
where
χ2 is the ordinary χ2 test statistic for independence in
contingency tables
N is the number of genes to be clustered
k is the number of clusters extracted
Stability score
0: no relationship
1: perfect reproducibility
Gota Morota
Reproducible Clusters from Microarray Research: Whither?
10. DYS875-006
Seminar
Approach to Compute Cluster Stability
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Microarray dataset with S subjects and N genes
Split dataset into “left” and “right” datasets
Left dataset with
S/2 subjects
Sub-sample left
dataset into sets of
various sample
sizes (3 to S/2)
Sub-sample right
dataset into sets of
various sample
sizes (3 to S/2)
Left sub-sampled
set of sample size
“x” (x ranges from
3 to S/2)
Right subsampled set of
sample size “x” (x
ranges from 3 to
S/2)
Cluster left set of
sample size “x” with
k (2 to 10) number
of clusters
Repeat 3
times
Right dataset with
S/2 subjects
Cluster right set of
sample size “x” with
k (2 to 10) number
of clusters
Compute Chi square (X2) between clustering results
Cluster Stability S(x,k) = Cramer’s v2
Figure 1
Algorithm: cluster stability computation
Algorithm: cluster stability computation. Cluster stability score S(x,k) is computed for every "k"(number of clusters) and
every pair of sub-sampled set of sample size "x".
./01!2!34!--
Figure 3: Cluster Stability Computation
/0+12$'3*42)$'&,$(&)$-%,+,%&'$03)0&.2.5
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Reproducible Clusters from Microarray Research: Whither?
11. Seminar
DYS875-006
Result on Real Datasets – (SOM)
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Table 3: Table showing stability results produced on a real dataset of sample size 16. Table 3 shows stability scores produced on a
given dataset of a sample size of n = 16. We split the dataset into two halves each containing 8 subjects. The left dataset is resampled
6 times producing 6 samples of sample sizes 3 to 8, respectively. Similarly the right dataset is resampled to produce 6 samples. We
measured the strength of the association between the clusters produced on every pair of samples (one sample from left and other
from right dataset both of same sample size) using Cramer's v2 . Columns in the table represent number of clusters (k) and rows
represent sample sizes. Stability score quantified for k = 10 and sample size 8 is 0.3699. This table shows there is 37% agreement
between the clusters produced (k = 10) on pair of samples (a sample from left dataset and other from right dataset both of sample size
8).
K (CLUSTERS)
2
SAMPLESIZE
3
4
5
6
7
8
3
4
5
6
7
8
9
10
0.5883
0.5799
0.5738
0.6433
0.6534
0.6759
0.47091
0.48045
0.48296
0.54638
0.54821
0.58447
0.4503
0.4244
0.4297
0.5142
0.5250
0.5520
0.4028
0.3894
0.3982
0.4727
0.4826
0.5045
0.3809
0.365
0.3644
0.4405
0.4462
0.4700
0.3600
0.3469
0.3430
0.4066
0.4211
0.4592
0.3313
0.3132
0.3195
0.3817
0.3915
0.4160
0.3107
0.297
0.3013
0.3616
0.3679
0.3975
0.2992
0.2858
0.2790
0.3396
0.348
0.3699
sample size. CLARA and Fuzzy C-means, however, mainwe deviate from k = 6, we observed a decline in stability
Figure scores until a sample size of a real scores. This phenomenon can 16
tained low stability4: Stability result on 30 was dataset of sample sizebe clearly observed in
attained. Stability scores then gradually increased after
CLARA, K-means and Fuzzy C-means (Figure 5). Hence,
this threshold. K-means and SOM showed superior stabilscores observed on k = 7 were always higher than that on
ity scores as compared to CLARA until the sample size
k = 2, since k = 7 is nearer to k = 6 (Figure 5). Figure 4
attained n = 30. It is interesting to note that average stabilshows results on simulated datasets for k = 6. We observed
ity achieved is not greater than 0.55 for all four clustering
the following differences in stability behaviors among the
Gota Morota
routines even when at sample size of n = 50 is attained.Reproducible Clustersalgorithms.
four clustering from Microarray Research: Whither?
12. ng methods. Alternatively, if
e of scores across 37 selected
scores from 37 real datasets)
epresent stability coefficients
clustering structure, we then
nd 0.8 until a sample size of
algorithms is achieved.
iors until sample size reached n = 100. K-means showed
Seminar
DYS875-006
high stability at smaller sample sizes as compared to the
other methods.
Result on Real Datasets – among different algorithms
Real datasets
Stability coefficient
0.5
SOM
0.4
Kmeans
0.3
Fuzzy C-means
0.2
Clara
0.1
48
38
43
33
28
23
18
8
13
0
3
the same clustering structure
tion ! set to (0.33)1/2 within
all datasets show high scores
her values of k. In simulated
utput tables produced on 8
e with each cell computed as
ding cells in 8 tables thereby
scores for each value of k (k
sub-sampled space. The final
ability behavior of the clusvalues of clusters (k) considwe produced a final output
2 to 10) across sub-sampled
esults for various values of k
n in Figure 5. As expected,
ed for the correct number of
tering routines thereby valiprogramming. However, as
0.6
Sample Size
Figure Stability results
Cluster 3
Cluster Stability results. Stability scores for various values of k (2 to 10) are computed on all 37 datasets. For each
dataset, we selected a column (k) showing maximum summation of scores across sample size. Finally all 37 columns
selected on 37 datasets were merged into one resultant column representing stability scores with respect to sample size
for that clustering routine.
Figure 5: Stability result on a real dataset of sample size 16
./01!2!34!-/0+12$'3*42)$'&,$(&)$-%,+,%&'$03)0&.2.5
Gota Morota
Reproducible Clusters from Microarray Research: Whither?
13. Seminar
DYS875-006
Result on Simulated Datasets – 1
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Figure 3 and Figure 4 suggest that
ble performance than other cluste
ered (SOM, CLARA and Fuzzy C
1.2
SOM showed similar behavior in
1
they are closely related to each oth
SOM
0.8
ids move freely in multidimension
K-means
0.6
constrained to a two-dimensiona
Fuzzy C-means
0.4
SOM, the distance of each input fr
Clara
0.2
is considered, instead of just the c
the neighborhood kernel [29]. Th
0
as conventional clustering algorit
Sample Size
neighborhood kernel is zero [29].
on all four clustering routines
microarray datasets, in general,
Figure Stability results on simulated datasets for k = 6
Cluster 4
structure. We do not claim that th
Cluster Stability results on simulated datasets for k =
the exact stability nature of a giv
6. Datasets are simulated with a clustering structure k = 6 (6
sample size, since these are genera
clusters). The above figure shows high stability scores
observed for k = 6 on all four clustering routines.
and variety of datasets. Nonetheles
consider performing cluster analys
to obtain more stable clustering s
• Stability
Fuzzy C-means simulated fluctuation
criterion for
6: K-means,evenresult onand SOM showeddataset forsuggests ofstatistical(k) for a givensm
k = 6a
in scores
at large sample sizes, whereas CLARA
number clusters
showed consistent behavior (constant level of scores) at
may be accomplished by computi
ous values of k and selecting that
larger sample sizes.
vides a maximum stability score fo
• CLARA maintained 100% stability for larger sample
We also evaluated stability perfo
sizes (300–500) whereas, SOM and Fuzzy C-means failed
Gota Morota
Reproducible Clusters from Microarray Research: Whither?
Figure
476
433
390
347
304
261
218
175
89
132
46
3
Stability coefficient
Simulated datasets for rho = sqrt(0.33) and k=6
14. Seminar
DYS875-006
Result on Simulated Datasets – 2
!"#$!%&%'(&)*+,%-.!"##$%!!&'())*!"+,'-#
Clara: rho = sqrt(0.33) & k=2 to 10
K-means: rho = sqrt(0.33) & k=2 to 10
2
1
3
0.8
4
0.6
5
0.4
6
0.2
7
8
498
465
432
399
366
333
300
267
234
201
168
69
10
Sample Size
Fuzzy C-means: rho = sqrt(0.33) & k=2 to 10
9
10
Sample Size
SOM: rho = sqrt(0.33) & k=2 to 10
Sample Size
2
3
0.6
4
0.4
5
0.2
6
7
498
465
432
399
366
333
300
267
234
201
168
135
69
0
36
8
1
0.8
102
498
465
432
399
366
333
300
7
267
0
234
6
201
0.2
168
5
135
0.4
69
4
36
3
102
2
0.6
3
1
0.8
Stability coefficient
1.2
3
1.2
Sability coefficient
135
9
36
0
102
498
465
432
399
366
333
8
300
0
267
7
234
0.2
201
6
168
0.4
69
5
135
0.6
36
4
102
3
3
1
0.8
1.2
Stability coefficient
2
3
Stability coefficient
1.2
9
Sample Size
Figure 5
Cluster Stability results on simulated datasets for k = 2 to k = 10
Cluster Stability results on simulated datasets for k = 2 to k = 10. Stability scores for various values of k (2 to 10) are
computed on all the 8 simulated datasets. For each dataset, we generate an output table of scores (explained in Algorithms
section). We merge all the 8 output tables produced into one table with each cell computed as average of corresponding cells
in 8 tables. Finally scores are plotted for all k values with respect to sample size. For cleaner visualization purposes, we do not
show stability curves for all k values in figure 5c and figure 5d. a Scores plotted for CLARA for each k (2–10). b Scores plotted
for K-means for each k (2–10). c Scores plotted for Fuzzy Cmeans for each k (2–10). d Scores plotted for SOM for each k (2–
10).
Gota Morota
Reproducible Clusters from Microarray Research: Whither?
15. DYS875-006
Seminar
Conclusion
microarray datasets may lack natural clustering structure
thereby producing low stability scores on all four methods
the algorithms studied may not be well suited to producing
reliable results
sample sizes typically used in microarray research may be too
small to support derivation of reliable clustering results
Gota Morota
Reproducible Clusters from Microarray Research: Whither?