This document describes a genetic algorithm approach for automatically generating fuzzy rules for classification problems. The genetic algorithm uses rule importance as the fitness criteria, calculated as the rule's support for each class. The algorithm encodes rules using fuzzy membership set numbers for antecedents and consequents. It iterates for a set number of generations or until a minimum number of rules are fired, selecting high-fitness rules to generate offspring via crossover. The offspring replace lower-fitness rules, and the new rule population is evaluated in the next generation. The approach aims to consistently generate optimal fuzzy rules for classification using genetic search.
The document discusses finding the optimal parameter K for the emerging pattern-based classifier PCL. PCL selects the top-K emerging patterns from each class that match a test instance and uses these patterns to predict the class label. The authors develop an algorithm to determine the best value of K for PCL on different datasets, as K's optimal value varies. They sample subsets of training data to find the K that performs best on average, maximizing the likelihood of good performance on the whole dataset. Experimental results show that finding the best K improves PCL's accuracy and that using incremental frequent pattern maintenance techniques speeds up the algorithm significantly.
A Mixed Discrete-Continuous Attribute List Representation for Large Scale Cla...jaumebp
This work assesses the performance of the BioHEL data mining method to handle large-scale datasets, and proposes a representation to deal efficiently with domains with mixed discrete-continuous attributes
A Study of Efficiency Improvements Technique for K-Means AlgorithmIRJET Journal
This document discusses techniques to improve the efficiency of the K-means clustering algorithm. It begins with an introduction to K-means clustering and discusses some of its limitations, such as high computational time. It then proposes using a ranking method to help assign data points to clusters more efficiently. The key steps of standard K-means clustering and the proposed ranking-based approach are described. Experimental results on sample datasets show that the ranking method leads to faster clustering compared to standard K-means, with comparable accuracy. Therefore, the ranking method can help enhance the performance of K-means clustering for large datasets.
Enhanced Genetic Algorithm with K-Means for the Clustering ProblemAnders Viken
The document proposes a genetic algorithm combined with K-Means clustering to solve clustering problems. The genetic algorithm is used to generate new clusters through crossover, then K-Means is applied to further improve cluster quality and speed up search. Experimental results showed the combined approach converges faster while producing equivalent quality clusters compared to a standard genetic algorithm alone.
This document summarizes a statistical analysis project using classification and prediction models to analyze charitable donation data. The goals were to 1) build a classification model to identify likely donors to maximize profit, and 2) develop a prediction model for donation amounts based on donor characteristics. Several models were tested on training, validation, and test datasets. The best classification model was a gradient boosting machine with an error rate of 11.4% and projected profit of $11,941.50. The best prediction model was a gradient boosting machine with a mean prediction error of 1.414.
Lazy Associative Classification is a novel lazy associative classifier that generates classification rules on demand for each test instance, focusing only on relevant features. This approach avoids overfitting and outperforms traditional eager associative classifiers and decision tree classifiers in experiments on 26 datasets. The lazy classifier is able to better handle small disjuncts problems compared to eager approaches and achieves higher accuracy with faster execution times.
This document analyzes gene expression data using five classification methods: logistic regression, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), K-nearest neighbors (KNN), and logistic generalized additive model (GAM). For each method, the author fits models to training data and validation data from a gene expression dataset, and calculates classification errors, sensitivities, and specificities. Based on the results, KNN performs best on the training data while LDA performs best on the validation data, with the lowest error rate and highest specificity, though it has only the second highest sensitivity.
This document describes a genetic algorithm approach for automatically generating fuzzy rules for classification problems. The genetic algorithm uses rule importance as the fitness criteria, calculated as the rule's support for each class. The algorithm encodes rules using fuzzy membership set numbers for antecedents and consequents. It iterates for a set number of generations or until a minimum number of rules are fired, selecting high-fitness rules to generate offspring via crossover. The offspring replace lower-fitness rules, and the new rule population is evaluated in the next generation. The approach aims to consistently generate optimal fuzzy rules for classification using genetic search.
The document discusses finding the optimal parameter K for the emerging pattern-based classifier PCL. PCL selects the top-K emerging patterns from each class that match a test instance and uses these patterns to predict the class label. The authors develop an algorithm to determine the best value of K for PCL on different datasets, as K's optimal value varies. They sample subsets of training data to find the K that performs best on average, maximizing the likelihood of good performance on the whole dataset. Experimental results show that finding the best K improves PCL's accuracy and that using incremental frequent pattern maintenance techniques speeds up the algorithm significantly.
A Mixed Discrete-Continuous Attribute List Representation for Large Scale Cla...jaumebp
This work assesses the performance of the BioHEL data mining method to handle large-scale datasets, and proposes a representation to deal efficiently with domains with mixed discrete-continuous attributes
A Study of Efficiency Improvements Technique for K-Means AlgorithmIRJET Journal
This document discusses techniques to improve the efficiency of the K-means clustering algorithm. It begins with an introduction to K-means clustering and discusses some of its limitations, such as high computational time. It then proposes using a ranking method to help assign data points to clusters more efficiently. The key steps of standard K-means clustering and the proposed ranking-based approach are described. Experimental results on sample datasets show that the ranking method leads to faster clustering compared to standard K-means, with comparable accuracy. Therefore, the ranking method can help enhance the performance of K-means clustering for large datasets.
Enhanced Genetic Algorithm with K-Means for the Clustering ProblemAnders Viken
The document proposes a genetic algorithm combined with K-Means clustering to solve clustering problems. The genetic algorithm is used to generate new clusters through crossover, then K-Means is applied to further improve cluster quality and speed up search. Experimental results showed the combined approach converges faster while producing equivalent quality clusters compared to a standard genetic algorithm alone.
This document summarizes a statistical analysis project using classification and prediction models to analyze charitable donation data. The goals were to 1) build a classification model to identify likely donors to maximize profit, and 2) develop a prediction model for donation amounts based on donor characteristics. Several models were tested on training, validation, and test datasets. The best classification model was a gradient boosting machine with an error rate of 11.4% and projected profit of $11,941.50. The best prediction model was a gradient boosting machine with a mean prediction error of 1.414.
Lazy Associative Classification is a novel lazy associative classifier that generates classification rules on demand for each test instance, focusing only on relevant features. This approach avoids overfitting and outperforms traditional eager associative classifiers and decision tree classifiers in experiments on 26 datasets. The lazy classifier is able to better handle small disjuncts problems compared to eager approaches and achieves higher accuracy with faster execution times.
This document analyzes gene expression data using five classification methods: logistic regression, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), K-nearest neighbors (KNN), and logistic generalized additive model (GAM). For each method, the author fits models to training data and validation data from a gene expression dataset, and calculates classification errors, sensitivities, and specificities. Based on the results, KNN performs best on the training data while LDA performs best on the validation data, with the lowest error rate and highest specificity, though it has only the second highest sensitivity.
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
This document summarizes a research paper that proposes a new Particle Swarm Optimization (PSO) based K-Prototype clustering algorithm to cluster mixed numeric and categorical data. It begins with background information on clustering algorithms like K-Means, K-Modes, and K-Prototype. It then describes the K-Prototype algorithm, PSO, and discrete binary PSO. Related work integrating PSO with other clustering algorithms is also reviewed. The proposed approach uses binary PSO to select improved initial prototypes for K-Prototype clustering in order to obtain better clustering results than traditional K-Prototype and avoid local optima.
This document compares classification and regression models using the CARET package in R. Four classification algorithms are evaluated on Titanic survival data and three regression algorithms are evaluated on property liability data. For classification, random forests performed best based on the F-measure metric. For regression, gradient boosted models performed best based on RMSE. The document concludes classification can predict Titanic survivor characteristics while regression can predict property hazards.
Sequence alignment involves arranging DNA, RNA, or protein sequences to identify similar regions and discover functional, structural, and evolutionary relationships. It compares a reference sequence to a query sequence. Alignments reveal regions of similarity that unlikely occurred by chance and may indicate common ancestry. Global alignment looks for conserved regions across full sequences while local alignment finds local matches between subsequences. Pairwise alignment involves two sequences while multiple sequence alignment handles three or more. Dynamic programming and word methods are common algorithmic approaches to sequence alignment.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Improving Spam Mail Filtering Using Classification Algorithms With Partition ...IRJET Journal
This document discusses improving spam mail filtering using various classification algorithms and a partition membership filter for preprocessing. It analyzes the performance of classification algorithms like JRip, Filtered Classifier, K-star, SGD, Multinomial, and Random Tree on a spam email dataset using metrics like accuracy, error rate, recall, and precision. The Random Tree algorithm achieved the best performance with 97.08% accuracy, 0.938 kappa statistics, and 0.56 seconds build time, outperforming the other algorithms. Preprocessing the data with a partition membership filter before classification further improved performance.
Genetic Programming for Generating Prototypes in Classification ProblemsTarundeep Dhot
This document summarizes a research paper that proposes using genetic programming to generate prototypes for classification problems. It describes encoding prototypes as variable-length logical expressions and using a multi-tree representation. The approach evolves a population of these prototypes using genetic operators like crossover and mutation. Classification involves matching samples to prototypes, and fitness is based on recognition rate on a training set. The method was tested on several datasets and achieved higher recognition rates than an alternative genetic programming approach.
The document discusses various feature subset selection methods for gene expression datasets, which have a large number of attributes and small number of samples. It describes filter methods like rank-based and space search-based approaches, as well as wrapper and embedded methods. Rank-based filters calculate correlations like Pearson and mutual information scores to select relevant features. Space search filters evaluate feature subsets for relevancy and redundancy. The document also discusses unsupervised feature selection using maximal information coefficient and affinity propagation clustering. It provides an example of applying feature selection to breast cancer subtyping using consensus clustering across multiple datasets.
A ROBUST MISSING VALUE IMPUTATION METHOD MIFOIMPUTE FOR INCOMPLETE MOLECULAR ...ijcsa
This document presents a new method called MiFoImpute for imputing missing values in molecular descriptor datasets. MiFoImpute uses an iterative random forest approach. It is compared to 10 other imputation methods on two molecular descriptor datasets with varying percentages of artificially introduced missing values (10-30%). Experimental results show that MiFoImpute has competitive or better performance than other methods according to NRMSE and NMAE error metrics. It exhibits robustness to increasing levels of missing data and computational efficiency compared to some other methods.
The document proposes a novel hybrid method called PCA-BEL for classifying gene expression microarray data. PCA-BEL uses principal component analysis (PCA) for feature extraction followed by classification using a Brain Emotional Learning (BEL) network. PCA reduces the dimensionality of the microarray data to overcome the high dimensionality problem. BEL is then used for classification due its low computational complexity making it suitable for high dimensional data. The method is tested on several cancer gene expression datasets and achieves average accuracies of 100%, 96%, 98.32%, 87.40% and 88% on five datasets respectively, demonstrating its effectiveness for microarray classification tasks.
Spam filtering by using Genetic based Feature SelectionEditor IJCATR
Spam is defined as redundant and unwanted electronica letters, and nowadays, it has created many problems in business life such as
occupying networks bandwidth and the space of user’s mailbox. Due to these problems, much research has been carried out in this
regard by using classification technique. The resent research show that feature selection can have positive effect on the efficiency of
machine learning algorithm. Most algorithms try to present a data model depending on certain detection of small set of features.
Unrelated features in the process of making model result in weak estimation and more computations. In this research it has been tried
to evaluate spam detection in legal electronica letters, and their effect on several Machin learning algorithms through presenting a
feature selection method based on genetic algorithm. Bayesian network and KNN classifiers have been taken into account in
classification phase and spam base dataset is used.
A survey on Efficient Enhanced K-Means Clustering Algorithmijsrd.com
Data mining is the process of using technology to identify patterns and prospects from large amount of information. In Data Mining, Clustering is an important research topic and wide range of unverified classification application. Clustering is technique which divides a data into meaningful groups. K-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. In this paper, we present the comparison of different K-means clustering algorithms.
This document summarizes key aspects of sequence alignment. It discusses how sequence alignment involves comparing sequences to find identical or similar characters in the same order. It describes global and local alignment and the algorithms used for each. It also discusses scoring systems for alignments, including penalties for gaps and mismatches. The goals of sequence alignment are to infer functional, structural or evolutionary relationships between sequences.
B.sc biochem i bobi u 3.2 algorithm + blastRai University
The Needleman-Wunsch algorithm is a dynamic programming algorithm used to align biological sequences like protein or DNA. It was developed in 1970 by Needleman and Wunsch and is still widely used for optimal global sequence alignment. The algorithm divides a sequence alignment problem into smaller subproblems and combines their solutions to find the optimal global alignment.
Sequence alignment involves arranging DNA, RNA, or protein sequences to identify regions of similarity. It is used to determine if sequences are evolutionarily related, observe patterns of conservation, and find similar regions within proteins. The key steps are representation of sequences in a matrix, insertion of gaps, and use of scoring schemes like PAM and BLOSUM matrices to identify the best alignment. Global alignment forces alignment over full sequence lengths while local alignment identifies short, well-matching segments. Algorithms like Needleman-Wunsch and Smith-Waterman use dynamic programming to calculate optimal pairwise sequence alignments.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
Abstract- The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
rule refinement in inductive knowledge based systemsarteimi
This document summarizes a paper about refining inductive knowledge-based systems through empirical methods. It describes a system called ARIS that interleaves learning and performance evaluation to allow accurate classifications on real-world datasets. ARIS employs an ordering of rules according to their learned weights, which are calculated using Bayes' theorem. The system focuses rule analysis and refinement by heuristics. It has been used to refine knowledge bases from C4.5 and RIPPER, showing improved accuracy and the ability to gradually enhance the knowledge base during refinement.
A Threshold Fuzzy Entropy Based Feature Selection: Comparative StudyIJMER
Feature selection is one of the most common and critical tasks in database classification. It
reduces the computational cost by removing insignificant and unwanted features. Consequently, this
makes the diagnosis process accurate and comprehensible. This paper presents the measurement of
feature relevance based on fuzzy entropy, tested with Radial Basis Classifier (RBF) network,
Bagging(Bootstrap Aggregating), Boosting and stacking for various fields of datasets. Twenty
benchmarked datasets which are available in UCI Machine Learning Repository and KDD have been
used for this work. The accuracy obtained from these classification process shows that the proposed
method is capable of producing good and accurate results with fewer features than the original
datasets.
IRJET-A Novel Approaches for Motif Discovery using Data Mining AlgorithmIRJET Journal
This document presents a novel approach for motif discovery using data mining algorithms. It proposes the MDWB algorithm which uses a word-based approach and MapReduce functions to identify motifs from large ChIP-seq datasets. The algorithm labels control and test datasets to determine thresholds and extracts frequently occurring substrings. MapReduce is used to reduce memory and time costs during the mining stage. Experimental results on ChIP-seq datasets show the MDWB algorithm achieves higher accuracy and runs faster than previous methods for identifying transcription factor binding sites.
Classification of Breast Cancer Diseases using Data Mining Techniquesinventionjournals
Medical data mining has great deal for exploring new knowledge from large amount of data. Classification is one of the important data mining techniques for classification of data. In this research work, we have used various data mining based classification techniques for classification of cancer diseases patient or not. We applied the Breast Cancer-Wisconsin (Original) data set into different data mining techniques and compared the accuracy of models with two different data partitions. BayesNet achieved highest accuracy as 97.13% in case of 10-fold data partitions. We have also applied the info gain feature selection technique on BayesNet and Support Vector Machine (SVM) and achieved best accuracy 97.28% accuracy with BayesNet in case of 6 feature subset.
This document presents a novel fuzzy k-nearest neighbor equality (FK-NNE) algorithm for classifying masses in mammograms as benign or malignant. The algorithm assigns membership values to different classes based on distances to k-nearest neighbors. It achieved 94.46% sensitivity, 96.81% specificity, and 96.52% accuracy, outperforming k-nearest neighbors, fuzzy k-nearest neighbors, and k-nearest neighbor equality algorithms. The algorithm considers relative importance of neighbors and assigns partial membership to classes, addressing issues with insufficient knowledge faced by other techniques. Experimental results demonstrated FK-NNE had the best performance with an area under the ROC curve of 0.9734, indicating high diagnostic accuracy.
The analysis of proteins and messenger RNA is commonly used in the comparison of gene expression patterns in tissues or cells of different types and under distinct conditions. In gene expression analysis, normalization is a critical step as it guarantees the validity of downstream analyses. Data preprocessing is an indispensable step in the extraction and normalization of microarray gene expression data. The normalization of gene expression data is essential in ensuring accurate inferences. A number of normalization methods in high throughput sequencing studies are being employed. The preprocessing activity begins by a careful analysis of the gene expression data and usually involves the classification of many raw signal intensities into one expression value. The Robust Multiarray Average (RMA) is a normalization approach for microarrays that involves background correction, normalization and summarization of probe levels information without using MM probes (Lim et al., 2007). It is an algorithm commonly used in the creation of an expression matrix for Affymetrix data and is one of the most commonly used modes of preprocessing to normalize gene expression data. Values of raw intensity are initially background corrected and log2 transformed before being normalized. In order to generate an expression measure for probe sets on each array, a linear model is fitted to the normalized data.
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
This document summarizes a research paper that proposes a new Particle Swarm Optimization (PSO) based K-Prototype clustering algorithm to cluster mixed numeric and categorical data. It begins with background information on clustering algorithms like K-Means, K-Modes, and K-Prototype. It then describes the K-Prototype algorithm, PSO, and discrete binary PSO. Related work integrating PSO with other clustering algorithms is also reviewed. The proposed approach uses binary PSO to select improved initial prototypes for K-Prototype clustering in order to obtain better clustering results than traditional K-Prototype and avoid local optima.
This document compares classification and regression models using the CARET package in R. Four classification algorithms are evaluated on Titanic survival data and three regression algorithms are evaluated on property liability data. For classification, random forests performed best based on the F-measure metric. For regression, gradient boosted models performed best based on RMSE. The document concludes classification can predict Titanic survivor characteristics while regression can predict property hazards.
Sequence alignment involves arranging DNA, RNA, or protein sequences to identify similar regions and discover functional, structural, and evolutionary relationships. It compares a reference sequence to a query sequence. Alignments reveal regions of similarity that unlikely occurred by chance and may indicate common ancestry. Global alignment looks for conserved regions across full sequences while local alignment finds local matches between subsequences. Pairwise alignment involves two sequences while multiple sequence alignment handles three or more. Dynamic programming and word methods are common algorithmic approaches to sequence alignment.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Improving Spam Mail Filtering Using Classification Algorithms With Partition ...IRJET Journal
This document discusses improving spam mail filtering using various classification algorithms and a partition membership filter for preprocessing. It analyzes the performance of classification algorithms like JRip, Filtered Classifier, K-star, SGD, Multinomial, and Random Tree on a spam email dataset using metrics like accuracy, error rate, recall, and precision. The Random Tree algorithm achieved the best performance with 97.08% accuracy, 0.938 kappa statistics, and 0.56 seconds build time, outperforming the other algorithms. Preprocessing the data with a partition membership filter before classification further improved performance.
Genetic Programming for Generating Prototypes in Classification ProblemsTarundeep Dhot
This document summarizes a research paper that proposes using genetic programming to generate prototypes for classification problems. It describes encoding prototypes as variable-length logical expressions and using a multi-tree representation. The approach evolves a population of these prototypes using genetic operators like crossover and mutation. Classification involves matching samples to prototypes, and fitness is based on recognition rate on a training set. The method was tested on several datasets and achieved higher recognition rates than an alternative genetic programming approach.
The document discusses various feature subset selection methods for gene expression datasets, which have a large number of attributes and small number of samples. It describes filter methods like rank-based and space search-based approaches, as well as wrapper and embedded methods. Rank-based filters calculate correlations like Pearson and mutual information scores to select relevant features. Space search filters evaluate feature subsets for relevancy and redundancy. The document also discusses unsupervised feature selection using maximal information coefficient and affinity propagation clustering. It provides an example of applying feature selection to breast cancer subtyping using consensus clustering across multiple datasets.
A ROBUST MISSING VALUE IMPUTATION METHOD MIFOIMPUTE FOR INCOMPLETE MOLECULAR ...ijcsa
This document presents a new method called MiFoImpute for imputing missing values in molecular descriptor datasets. MiFoImpute uses an iterative random forest approach. It is compared to 10 other imputation methods on two molecular descriptor datasets with varying percentages of artificially introduced missing values (10-30%). Experimental results show that MiFoImpute has competitive or better performance than other methods according to NRMSE and NMAE error metrics. It exhibits robustness to increasing levels of missing data and computational efficiency compared to some other methods.
The document proposes a novel hybrid method called PCA-BEL for classifying gene expression microarray data. PCA-BEL uses principal component analysis (PCA) for feature extraction followed by classification using a Brain Emotional Learning (BEL) network. PCA reduces the dimensionality of the microarray data to overcome the high dimensionality problem. BEL is then used for classification due its low computational complexity making it suitable for high dimensional data. The method is tested on several cancer gene expression datasets and achieves average accuracies of 100%, 96%, 98.32%, 87.40% and 88% on five datasets respectively, demonstrating its effectiveness for microarray classification tasks.
Spam filtering by using Genetic based Feature SelectionEditor IJCATR
Spam is defined as redundant and unwanted electronica letters, and nowadays, it has created many problems in business life such as
occupying networks bandwidth and the space of user’s mailbox. Due to these problems, much research has been carried out in this
regard by using classification technique. The resent research show that feature selection can have positive effect on the efficiency of
machine learning algorithm. Most algorithms try to present a data model depending on certain detection of small set of features.
Unrelated features in the process of making model result in weak estimation and more computations. In this research it has been tried
to evaluate spam detection in legal electronica letters, and their effect on several Machin learning algorithms through presenting a
feature selection method based on genetic algorithm. Bayesian network and KNN classifiers have been taken into account in
classification phase and spam base dataset is used.
A survey on Efficient Enhanced K-Means Clustering Algorithmijsrd.com
Data mining is the process of using technology to identify patterns and prospects from large amount of information. In Data Mining, Clustering is an important research topic and wide range of unverified classification application. Clustering is technique which divides a data into meaningful groups. K-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. In this paper, we present the comparison of different K-means clustering algorithms.
This document summarizes key aspects of sequence alignment. It discusses how sequence alignment involves comparing sequences to find identical or similar characters in the same order. It describes global and local alignment and the algorithms used for each. It also discusses scoring systems for alignments, including penalties for gaps and mismatches. The goals of sequence alignment are to infer functional, structural or evolutionary relationships between sequences.
B.sc biochem i bobi u 3.2 algorithm + blastRai University
The Needleman-Wunsch algorithm is a dynamic programming algorithm used to align biological sequences like protein or DNA. It was developed in 1970 by Needleman and Wunsch and is still widely used for optimal global sequence alignment. The algorithm divides a sequence alignment problem into smaller subproblems and combines their solutions to find the optimal global alignment.
Sequence alignment involves arranging DNA, RNA, or protein sequences to identify regions of similarity. It is used to determine if sequences are evolutionarily related, observe patterns of conservation, and find similar regions within proteins. The key steps are representation of sequences in a matrix, insertion of gaps, and use of scoring schemes like PAM and BLOSUM matrices to identify the best alignment. Global alignment forces alignment over full sequence lengths while local alignment identifies short, well-matching segments. Algorithms like Needleman-Wunsch and Smith-Waterman use dynamic programming to calculate optimal pairwise sequence alignments.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
Abstract- The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
rule refinement in inductive knowledge based systemsarteimi
This document summarizes a paper about refining inductive knowledge-based systems through empirical methods. It describes a system called ARIS that interleaves learning and performance evaluation to allow accurate classifications on real-world datasets. ARIS employs an ordering of rules according to their learned weights, which are calculated using Bayes' theorem. The system focuses rule analysis and refinement by heuristics. It has been used to refine knowledge bases from C4.5 and RIPPER, showing improved accuracy and the ability to gradually enhance the knowledge base during refinement.
A Threshold Fuzzy Entropy Based Feature Selection: Comparative StudyIJMER
Feature selection is one of the most common and critical tasks in database classification. It
reduces the computational cost by removing insignificant and unwanted features. Consequently, this
makes the diagnosis process accurate and comprehensible. This paper presents the measurement of
feature relevance based on fuzzy entropy, tested with Radial Basis Classifier (RBF) network,
Bagging(Bootstrap Aggregating), Boosting and stacking for various fields of datasets. Twenty
benchmarked datasets which are available in UCI Machine Learning Repository and KDD have been
used for this work. The accuracy obtained from these classification process shows that the proposed
method is capable of producing good and accurate results with fewer features than the original
datasets.
IRJET-A Novel Approaches for Motif Discovery using Data Mining AlgorithmIRJET Journal
This document presents a novel approach for motif discovery using data mining algorithms. It proposes the MDWB algorithm which uses a word-based approach and MapReduce functions to identify motifs from large ChIP-seq datasets. The algorithm labels control and test datasets to determine thresholds and extracts frequently occurring substrings. MapReduce is used to reduce memory and time costs during the mining stage. Experimental results on ChIP-seq datasets show the MDWB algorithm achieves higher accuracy and runs faster than previous methods for identifying transcription factor binding sites.
Classification of Breast Cancer Diseases using Data Mining Techniquesinventionjournals
Medical data mining has great deal for exploring new knowledge from large amount of data. Classification is one of the important data mining techniques for classification of data. In this research work, we have used various data mining based classification techniques for classification of cancer diseases patient or not. We applied the Breast Cancer-Wisconsin (Original) data set into different data mining techniques and compared the accuracy of models with two different data partitions. BayesNet achieved highest accuracy as 97.13% in case of 10-fold data partitions. We have also applied the info gain feature selection technique on BayesNet and Support Vector Machine (SVM) and achieved best accuracy 97.28% accuracy with BayesNet in case of 6 feature subset.
This document presents a novel fuzzy k-nearest neighbor equality (FK-NNE) algorithm for classifying masses in mammograms as benign or malignant. The algorithm assigns membership values to different classes based on distances to k-nearest neighbors. It achieved 94.46% sensitivity, 96.81% specificity, and 96.52% accuracy, outperforming k-nearest neighbors, fuzzy k-nearest neighbors, and k-nearest neighbor equality algorithms. The algorithm considers relative importance of neighbors and assigns partial membership to classes, addressing issues with insufficient knowledge faced by other techniques. Experimental results demonstrated FK-NNE had the best performance with an area under the ROC curve of 0.9734, indicating high diagnostic accuracy.
The analysis of proteins and messenger RNA is commonly used in the comparison of gene expression patterns in tissues or cells of different types and under distinct conditions. In gene expression analysis, normalization is a critical step as it guarantees the validity of downstream analyses. Data preprocessing is an indispensable step in the extraction and normalization of microarray gene expression data. The normalization of gene expression data is essential in ensuring accurate inferences. A number of normalization methods in high throughput sequencing studies are being employed. The preprocessing activity begins by a careful analysis of the gene expression data and usually involves the classification of many raw signal intensities into one expression value. The Robust Multiarray Average (RMA) is a normalization approach for microarrays that involves background correction, normalization and summarization of probe levels information without using MM probes (Lim et al., 2007). It is an algorithm commonly used in the creation of an expression matrix for Affymetrix data and is one of the most commonly used modes of preprocessing to normalize gene expression data. Values of raw intensity are initially background corrected and log2 transformed before being normalized. In order to generate an expression measure for probe sets on each array, a linear model is fitted to the normalized data.
Application of support vector machines for prediction of anti hiv activity of...Alexander Decker
This document describes a study that used support vector machines (SVM) to develop a quantitative structure-activity relationship (QSAR) model to predict the anti-HIV activity of TIBO derivatives. The SVM model achieved high correlation (q2=0.96) and low error (RMSE=0.212), outperforming artificial neural networks and multiple linear regression models developed on the same data set. The results indicate that SVM is a valuable tool for QSAR modeling and predicting anti-HIV activity of chemical compounds.
The document provides an overview of computational methods for sequence alignment. It discusses different types of sequence alignment including global and local alignment. It also describes various methods for sequence alignment, such as dot matrix analysis, dynamic programming algorithms (e.g. Needleman-Wunsch, Smith-Waterman), and word/k-tuple methods. Scoring matrices like PAM and BLOSUM that are used for sequence alignments are also explained.
Vchunk join an efficient algorithm for edit similarity joinsVijay Koushik
Similarity join is most important technique to
involve many applications such as data integration, record
linkage and pattern recognition. Here we introduce new
algorithm for similarity join with edit distance constraints.
Currently extracting overlapping grams from string and consider
only string that share certain gram as candidate. Now we propose
extracting non-overlapping substring or chunk from string.
Chunk scheme based on tail-restricted chunk boundary
dictionary (CBD). This approach integrated existing approach
for calculating similarity with several new filters unique to chunk
based method. Greedy algorithm automatically select good
chunking scheme from given data set. Then show the result our
method occupies less space and faster performance to compute
the value
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
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1. International Journal of Engineering and Technical Research (IJETR)
ISSN: 2321-0869 (O) 2454-4698 (P), Volume-5, Issue-3, July 2016
25 www.erpublication.org
Abstract— Mining of association rules in the field of
bioinformatics is essential for the researchers to discover the
relationship between different genes. A large number of
association rules generatetd by the conventional rule mining
approaches creates a problem for selecting top among them.
Rank-Based Weighted Association Rule-Mining (RANWAR) is
used for rule ranking by implementing two measures i.e.
weighted condensed support (wcs) and weighted condensed
confidence(wcc) which is used to solve this problem. The
proposed system helps to find out top gene among all the present
gene in the system. The results are generated through rule
generation algorithm which enhances the system outcome, by
implementing RANWAR algorithm. This system implements
Adaptive K-means clustering for rule mining in the proposed
system. This technique for clustering relies on K -means such
that the knowledge is partitioned into K clusters. During this
technique, the number of clusters is predefined and therefore the
technique is extremely dependent on the initial identification of
components that represent the clusters well.
Index Terms— RANWAR, Gene-rank, gene-weight, wcc, wcs,
weighted association rule generation.
I. INTRODUCTION
In the area of data mining the rule generation methods are
utilized to determine relationships between different items.
The ranking of genes results is utilized to research about the
bioinformatics for the avoidance of particular health
problems. In the proposed framework rule mining is done
with the help of weighted support and and weighted
confidence of the record of generated outcomes. In this paper
a weighted rule mining method i.e. RANWAR has been used
and adaptive k-means clustering has been proposed. In some
cases it may happen that various rules hold the same value for
support as well as confidence. In such circumstance, if it is
required to select couple of them, it becomes difficult . This
issue is solved with the help of RANWAR. The benefit of this
method is that it generates very less number of item-sets as
compared to traditional algorithms used for association rule
mining. It has been observed that there’s no such ARM
method that generates less amount of frequent itemsets than
RANWAR. Along these lines, it requires less measure of time
contrasted with different algorithms. One more point of
preference of this algorithm is that a some of the generated
rules that hold poor rank in traditional rule generating
algorithms, hold top position i.e. rank in RANWAR.
In this framework basically the differentially expressed
(DE) and differentially methylated (DM) genes have been
used. For this, Limma is the related accommodating
implemented mathematics that performs great, for normal and
Deepti Ambaselkar, RajarshiShahu College of Engineering, Pune, India
Dr. A. B. Bagwan, RajarshiShahu College of Engineering, Pune, India
non normal distribution of information for each type of
information size (i.e. little, medium, vast). Along with this
rank is provided to each gene in the genelist obtained from
Limma based on their p-values. After providing rank to genes
weight is assigned to each genes which is calculated with the
help of their respective ranks [1]. In this way, importance to
each gene is provided. The measures used here i.e. wcs and
wcc are condensed style of the ordinary support and
confidence. A near examination of this framework [1] has
been made with the Apriori algorithmic rule and other rule
mining algorithms.
Selection of top rules between huge amounts of rules is
always a problematic procedure.This issue is overcome by
applying RANWAR algorithm. But there is a need of
improvement in the accuracy of generating rules. Hence, we
have proposed the adaptive k-means clustering approach in
order to form the initial clusters more accurately as compared
to k-means clustering.
II. LITERATURE SURVEY
Authors have composed [1] two new weighted condensed
rule-interestingness measures on the basis of ranks such as
wcs and wcc. RANWAR which is one of the weighted
algorithm for mining of rules has been composed with these
measures. To ascertain p-value of gene it makes utilization of
statistical test, Limma and some weight is given to every gene
on the basis of their p-value ranking. Authors utilized couple
of gene expression and additionally two methylation datasets
to obtain the contrast between efficiency of RANWAR with
the other ARM algorithms. It delivers low measure of
frequent sets of item contrasted with rest of the algorithms. In
such way the RANWAR decreases time of the algorithm
execution.
Authors approached [2] a problem consist in the mining
association rules in client transaction in an immense dataset as
in sets of items. Authors have made an exploration on finding
down legitimate rules that have minimum transactional and
minimum certainty. They found the rules that have single item
inside the subsequent and a union of any number of items in
the precursor. They have tackled this issue by decaying it
inside two sub issues as searching those item-sets which are
available at minimum.
Authors led an observation [4] for the prediction of Uterine
Leiomyoma of statistical methods and association rules on
mRNA expression and DNA methylation datasets. Authors
likewise proposed a rule-base classifier which is one of a kind.
Authors utilized a Genetic Algorithm based rank aggregation
approach over the association rules which is the outcome from
the training information by Apriori association rule mining
algorithm on the basis of 16 different rule-interestingness
measures.
Author attempted [6] to expand regarded the unique concept
and methods utilized for mining association rule from the
genomic information have been clarified additionally as of
Adaptive K-means clustering for Association Rule
Mining from Gene Expression Data
Deepti Ambaselkar, Dr. A. B. Bagwan
2. Adaptive K-means clustering for Association Rule Mining from Gene Expression Data
26 www.erpublication.org
late exhibited association rule mining methods have been
checked on and talked about.
III. IMPLEMENTATION DETAILS
Identifying Differentially Expressed/ Methylated
Genes and their Ranking
As it is known that gene dataset contains a large amount of
genes, so, some pre-filtering method is applied on the dataset
(i.e. genes with low change are eliminated). In case, due to
low variance of genes, regularly lesser p-value is produced
that seem to be significant, however in real world it is not.
Subsequently, the general contrast in the information
relatively for each previous gene must be inspected and strain
the genes having to massively least difference. The separated
data must be normalized gene-wise since normalization
changes the information from totally various scales into a
standard scale. Here, zero-mean normalization has been
utilized that changes over information in such a way that mean
of each gene gets to be zero and standard deviation of each
gene gets to be one.
With a specific end goal to perceive DE and DM genes, a
non-parametric test is needed, to be appropriately
implemented. In this manner, Limma has been chosen since it
performs better for every typical as well as non-typical
conveyance for the whole volumes of information.
In any case, on the idea of value got from t-statistic,
correlated p-value is calculated from either aggregate
circulation function (cdf) [8] or t-table. If p-value is less than
0.05, then that gene is DE/DM, else it is not. At that point rank
is given to these genes on the basis of their acquired p-values.
Assignment of Weight to Every Gene
In this technique, each gene doesn’t have same importance.
Subsequently, some weight or value is allocated to each gene
according to their ranking. The weights of all genes are
calculated in a way such that the variation in weights of any of
the two genes that is consecutively ranked is similar and the
weight of the gene that is basically ranked first is one. The
points of range of assigned weights lie between zero and one.
If the total number of genes in the system are “n” . Then,
weight of each gene (shown by 1 i n ) is computed from
a function of rank (indicated by ri 1 i n ) and number of
genes.
Discretization of Data
Assume that input is the information matrix shown as I[r,c]
where, r shown genes and c shown samples. To start with, the
transpose of matrix is executed. Presently, procedure of
discretization for the input information mtrix is need for
deploying association rule mining. With the end goal of
discretization, here Adaptive k-implies clustering rule is
utilized by us.Reducing the iterations is the aim behind the use
of Adaptive using K - means clustering. The concept is to
define k-centres for every cluster. In next step association of
every data point with its nearest centre after computing the
distance from every centres selected. The distance is known
as Euclidean distance. Early age group is the stage which
comes after completion of first iteration (when every point is
covered and no points are pending). Here we compute k-new
centroids as centre of clusters resulting from the last step and
once more novel binding is finished by computing the
distance.The k-means algorithm starts at beginning cluster
centroids, and iteratively alters these centroids to minimize
the objective function. The k-means always collect to a local
minimum. The specific local minimum found based on the
beginning cluster centroids.
Steps involved in adaptive k-means clustering are given
bellow,
Step1: Choose number of clusters for clustering.
Step2: Get all elements for clustering.
step3: Select an element randomly to get count between two
elements from each existing elements.
step4: Form clusters where minimum distance of count found
of each elements.
step5: Get all the initial clusters by sorting each element into
existing clusters and get mean(centroid).
step6: Calculate mean of each clusters.
step7: Compare each mean of cluster with other elements of
cluster.
step8: If distance is less between mean and element of other
cluster shift the element into specific cluster.
step9: Calculate again mean of existing cluster.
step10: Repeat the previous step as per given iterations.
Rule Mining and Identifying Frequent Itemset
After the post-discretization procedure, it is expected to
spot frequent item-sets. For this, at in the first place,
assessment of 1-itemsets is done, so to decide the frequent
singleton item-sets (i.e. wcs of these singleton item-sets is
more prominent than the support threshold min_wsupp).
Essentially, their supersets i.e. 2-itemsets are computed to
affirm the 2-itemsets that are repetitive. At that point
extraction of rules is done from these itemsets. From that
point, weighted condensed confidence for every single rule is
calculated. The rule with their values of wcc being greater
than or equivalent to the defined confidence threshold
(min_wconf). Then, their supersets i.e. 3-itemsets are
resolved after which visit 3-itemsets are resolved and
afterward from these item-sets noteworthy or say, essential
rules are separated. The algorithmic rule stops if there are no
extra augmentations of repeated item-sets to be considered.
At long last, the rules included are ranked depends on their
wcs or wcc. For points of interest, see algorithmic rule 1,
which is fundamentally an upgraded and weighted version of
Apriori [1].
Fig. 1.The proposed approach for rule mining from
biological data.
A. Algorithm
Input: Data matrix, D(genes, samples),original gene-list A1
as per the D, rank-wisegene-list A2(as per p-value), flag to
sort theevolved rules sort flag (based on either wcs orwcc ),
3. International Journal of Engineering and Technical Research (IJETR)
ISSN: 2321-0869 (O) 2454-4698 (P), Volume-5, Issue-3, July 2016
27 www.erpublication.org
minimum value of threshold for supportmin_wsupp ,
minimum value of threshold for confidence min_wconf.
Output: Rule-set Rules, support RuleSupp,confidence
RuleConf.
1) start
2) Normalize D with zero-mean normalization.
3) Evaluate gene-rank (i.e.rankk(:) ) as per listof genes i.e.
A1.
4) Allot weight wt(:) to each gene as per theirrank rankk(:).
5) Take transpose of the normalized matrix.
6) Select initial values of seed for usingAdaptive k-means
clustering.
7) Discretize transposed data-matrix byapplying adaptive
k-means clusteringsample-wise.
8) Apply the technique of post-discretization.
9) Initialize k =1.
10) Discover frequent 1-itemsets,
}supmin_)(1|{ pwiwcsAiiFIk
11) repeat
12) Increment k by 1.
13) Produce itemsets that are candidate, CIkfrom
FIk-1itemsets.
14) for all itemsets that are candidate c CIk,
do
15) Evaluate wcs(c) for every c.
16) ifwcs(c) min_wsupp then
17) ];[ cFIFI kk
18) Produce rules, rule(:) from the frequentitemset, c.
19) Find out wcc(:) for every rule(:).
20) for all the evolved rules, r rule(:) do
21) ifwcc(r) >= min_wconf then
22) Accumulate the r in the resultant rule-listalong with its
wcs as well as wcc; Rules r RuleSupp wcs(r) and
RuleConf wcc(r) .
23) end of if
24) end of for
25) end of if
26) end of for
27) until
28) end of procedure
B. Mathematical Model
Normalization:
The zero-mean normalization can be shown as:
/)( ij
norm
ij XX
where, ijX
and
norm
ijX
represents the gene-value at ith
position and jth
sample before and after being normalized.
is the arithmetic mean and is the standard deviation.
Limma Test:
The tempered t-statistic in Limma is shown as:
)/()/1()/1(/1( 21 ggg snnt
Assigning Weight:
The formula for calculation of weight is:
))1((
1
ii rn
n
w
where wi is the calculated weight of ith
gene and ri is the rank
of ith gene in the system.
C. Experimental Setup
This system developed on Java framework(version jdk 6)
and Netbeans (version 6.9)is used as a development tool on
Windowsplatform. System is able to run on commonmachine
also it does not require any specifichardware.
IV. EXPERIMENTS AND RESULTS
Fig. 2.Comparison of accuracy of k-means and adaptive
k-means.
As the quantity of genes in every dataset is extensive thus,
Limma test is performed on the dataset which is normalized
and after that the system is introduced to construct a rank-wise
list of genes from best to most pessimistic scenario in
accordance with their p-values inside of the test. It is
determined as that intensively (statistically) essential genes
will be vital to the comparing disease for medical science
data. Along these lines, expecting all genes of an outsized
dataset may expand time period greatly and that it can take an
outsized collection of rules inside of which the majority of
them are excess/irrelevant. In this manner, it is proposed to
consider high genes and create exclusively most
indispensable rules instead of an outsized set of repetitive
rules. Here, this system has exhibited to set entirely various
accomplished diverse shorts of p-value for different datasets
for classifying a few numbers of high reviewed genes.
Fig. 3. Comparison of time of k-means and adaptive
k-means.
V. CONCLUSION
Top record is gained by using rule generation operation for
gene expression in proposed system. In proposed system we
deploy some of helpful strategies like Limma test, p-value,
4. Adaptive K-means clustering for Association Rule Mining from Gene Expression Data
28 www.erpublication.org
gene ranking etc.As a contribution adaptive k-means
clustering is used rather than k-means clustering operation to
solve some issues of previous system. System performance is
improved by using Adaptive k-means clustering and proper
outcomes are generated by using RANWAR algorithm.
RANWAR algorithm performs more effectively as compared
with Apriori rule generation algorithm. Systems efficiency
and difficulty is calculated by providing two gene expression
datasets.This system has tendency to collect top rules which
are extracted from the deployed system to make accessible for
research and analysis.
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