What is cluster analysis
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Cluster analysis is an unsupervised machine learning technique that groups unlabeled data points into clusters. The goal is to categorize data objects such that objects within a cluster are as similar as possible to each other, and as dissimilar as possible to objects in other clusters. Good clustering produces high quality clusters with high intra-class similarity and low inter-class similarity. Clustering has applications in marketing, land use analysis, insurance, and other domains.
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Cluster Analysis Introduction
Cluster analysis is a technique used to classify objects into groups called clusters based on their similarities. It has many applications in areas like market research, biology, and image processing. There are different types of clustering methods like partitioning, hierarchical, density-based, and grid-based. The k-means algorithm is a commonly used partitioning method where objects are grouped into k clusters based on their distances from centroid points, which are recalculated in each iteration until cluster memberships stabilize. Cluster analysis helps discover patterns and insights from large datasets.
Clustering
This document discusses unsupervised machine learning classification through clustering. It defines clustering as the process of grouping similar items together, with high intra-cluster similarity and low inter-cluster similarity. The document outlines common clustering algorithms like K-means and hierarchical clustering, and describes how K-means works by assigning points to centroids and iteratively updating centroids. It also discusses applications of clustering in domains like marketing, astronomy, genomics and more.
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This document provides an introduction to statistical model selection. It discusses various approaches to model selection including predictive risk, Bayesian methods, information theoretic measures like AIC and MDL, and adaptive methods. The key goals of model selection are to understand the bias-variance tradeoff and select models that offer the best guaranteed predictive performance on new data. Model selection aims to find the right level of complexity to explain patterns in available data while avoiding overfitting.
Types of clustering and different types of clustering algorithms
The document discusses different types of clustering algorithms:
1. Hard clustering assigns each data point to one cluster, while soft clustering allows points to belong to multiple clusters.
2. Hierarchical clustering builds clusters hierarchically in a top-down or bottom-up approach, while flat clustering does not have a hierarchy.
3. Model-based clustering models data using statistical distributions to find the best fitting model.
It then provides examples of specific clustering algorithms like K-Means, Fuzzy K-Means, Streaming K-Means, Spectral clustering, and Dirichlet clustering.
Cluster analysis
The document provides an overview of topics to be covered in a data analysis course, including cluster analysis and decision trees. The course will cover descriptive statistics, probability distributions, correlation, regression, hypothesis testing, clustering methods like k-means, and decision tree techniques like CHAID. Clustering involves grouping similar objects together to identify homogeneous clusters that are heterogeneous from each other. Applications of clustering include market segmentation, credit risk analysis, and operations. The document gives an example of clustering students based on their exam scores.
CART – Classification & Regression Trees
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Support vector machines (svm)
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
Hierarchical Clustering
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Cluster Analysis Introduction
Cluster analysis is a technique used to classify objects into groups called clusters based on their similarities. It has many applications in areas like market research, biology, and image processing. There are different types of clustering methods like partitioning, hierarchical, density-based, and grid-based. The k-means algorithm is a commonly used partitioning method where objects are grouped into k clusters based on their distances from centroid points, which are recalculated in each iteration until cluster memberships stabilize. Cluster analysis helps discover patterns and insights from large datasets.
Clustering
This document discusses unsupervised machine learning classification through clustering. It defines clustering as the process of grouping similar items together, with high intra-cluster similarity and low inter-cluster similarity. The document outlines common clustering algorithms like K-means and hierarchical clustering, and describes how K-means works by assigning points to centroids and iteratively updating centroids. It also discusses applications of clustering in domains like marketing, astronomy, genomics and more.
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This document provides an introduction to statistical model selection. It discusses various approaches to model selection including predictive risk, Bayesian methods, information theoretic measures like AIC and MDL, and adaptive methods. The key goals of model selection are to understand the bias-variance tradeoff and select models that offer the best guaranteed predictive performance on new data. Model selection aims to find the right level of complexity to explain patterns in available data while avoiding overfitting.
Types of clustering and different types of clustering algorithms
The document discusses different types of clustering algorithms:
1. Hard clustering assigns each data point to one cluster, while soft clustering allows points to belong to multiple clusters.
2. Hierarchical clustering builds clusters hierarchically in a top-down or bottom-up approach, while flat clustering does not have a hierarchy.
3. Model-based clustering models data using statistical distributions to find the best fitting model.
It then provides examples of specific clustering algorithms like K-Means, Fuzzy K-Means, Streaming K-Means, Spectral clustering, and Dirichlet clustering.
Cluster analysis
The document provides an overview of topics to be covered in a data analysis course, including cluster analysis and decision trees. The course will cover descriptive statistics, probability distributions, correlation, regression, hypothesis testing, clustering methods like k-means, and decision tree techniques like CHAID. Clustering involves grouping similar objects together to identify homogeneous clusters that are heterogeneous from each other. Applications of clustering include market segmentation, credit risk analysis, and operations. The document gives an example of clustering students based on their exam scores.
CART – Classification & Regression Trees
This document discusses Classification and Regression Trees (CART), a data mining technique for classification and regression. CART builds decision trees by recursively splitting data into purer child nodes based on a split criterion, with the goal of minimizing heterogeneity. It describes the 8 step CART generation process: 1) testing all possible splits of variables, 2) evaluating splits using reduction in impurity, 3) selecting the best split, 4) repeating for all variables, 5) selecting the split with most reduction in impurity, 6) assigning classes, 7) repeating on child nodes, and 8) pruning trees to avoid overfitting.
Support vector machines (svm)
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
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- Decision trees classify records by sorting them down the tree from root to leaf node, where each leaf represents a classification outcome.
- Trees are constructed top-down by selecting the most informative attribute to split on at each node, usually based on information gain.
- Trees can handle both numerical and categorical data and produce classification rules from paths in the tree.
- Examples of decision tree algorithms like ID3 that use information gain to select the best splitting attribute are described. The concepts of entropy and information gain are defined for selecting splits.
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Cluster Analysis
Cluster analysis is an unsupervised machine learning technique that groups unlabeled data points into clusters based on similarities. It aims to divide data into meaningful groups (clusters) where items in the same cluster are more similar to each other than items in different clusters. The document discusses different types of cluster analysis methods including hierarchical agglomerative clustering which starts with each data point as its own cluster and merges them together based on similarity, and k-means clustering which partitions data into k mutually exclusive clusters where each observation belongs to the cluster with the nearest mean. It also covers topics like distance measures, selecting the optimal number of clusters, and limitations of cluster analysis techniques.
3.3 hierarchical methods
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Cluster analysis
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Cluster analysis is a descriptive technique that groups similar objects into clusters. It finds natural groupings within data according to characteristics in the data. Cluster analysis is used for taxonomy development, data simplification, and relationship identification. Some applications of cluster analysis include market segmentation in marketing, grouping users on social networks, and reducing markers on maps. It requires representative data and assumes groups will be sufficiently sized and not distorted by outliers.
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Cluster analysis is an unsupervised machine learning technique that groups unlabeled data points into clusters based on similarities. It aims to divide data into meaningful groups (clusters) where items in the same cluster are more similar to each other than items in different clusters. The document discusses different types of cluster analysis methods including hierarchical agglomerative clustering which starts with each data point as its own cluster and merges them together based on similarity, and k-means clustering which partitions data into k mutually exclusive clusters where each observation belongs to the cluster with the nearest mean. It also covers topics like distance measures, selecting the optimal number of clusters, and limitations of cluster analysis techniques.
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Cluster analysis is a technique used to group objects based on characteristics they possess. It involves measuring the distance or similarity between objects and grouping those that are most similar together. There are two main types: hierarchical cluster analysis, which groups objects sequentially into clusters; and nonhierarchical cluster analysis, which directly assigns objects to pre-specified clusters. The choice of method depends on factors like sample size and research objectives.
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CSA 3702 machine learning module 3
This document provides an overview of clustering and k-means clustering algorithms. It begins by defining clustering as the process of grouping similar objects together and dissimilar objects separately. K-means clustering is introduced as an algorithm that partitions data points into k clusters by minimizing total intra-cluster variance, iteratively updating cluster means. The k-means algorithm and an example are described in detail. Weaknesses and applications are discussed. Finally, vector quantization and principal component analysis are briefly introduced.
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Head of Data Science, Etsy
Latent factor models and decision tree based models are widely used in tasks of prediction, ranking and recommendation. Latent factor models have the advantage of interpreting categorical features by a low-dimensional representation, while such an interpretation does not naturally fit numerical features. In contrast, decision tree based models enjoy the advantage of capturing the nonlinear interactions of numerical features, while their capability of handling categorical features is limited by the cardinality of those features. Since in real-world applications we usually have both abundant numerical features and categorical features with large cardinality (e.g. geolocations, IDs, tags etc.), we design a new model, called GB-CENT, which leverages latent factor embedding and tree components to achieve the merits of both while avoiding their demerits. With two real-world data sets, we demonstrate that GB-CENT can effectively (i.e. fast and accurately) achieve better accuracy than state-of-the-art matrix factorization, decision tree based models and their ensemble
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This document discusses using machine learning approaches to accurately detect, predict, segment and classify tumors from medical image data. It provides an overview of supervised learning and classification techniques like support vector machines, K-nearest neighbors, and decision trees. It notes that deep learning algorithms like convolutional neural networks have shown promising performance in medical domains. The objective is to develop a system that can analyze medical image data to detect cancer. It tests various classifiers on a dataset from the UCI repository containing 57 features from 32 instances, obtaining an accuracy of 42.857% using a support vector machine classifier.
Project 0th Review
This document proposes a combined clustering approach using the Gravitational Search Algorithm (GSA) and K-means (GSA-KM) along with genetic algorithms. It begins with an introduction to data mining and clustering. Then, it discusses the existing K-means and GSA algorithms and their limitations. The proposed GSA-KM approach applies K-means initially to generate centroids, then uses GSA to refine the clusters. Genetic algorithms are added to further improve efficiency and speed. The approach is implemented in C# using an MS Access database to cluster datasets and compare performance against other algorithms.
Birch1
Birch is an efficient data clustering algorithm for large datasets. It builds a CF-tree from one pass over the data, then performs clustering in memory. This allows it to cluster large datasets with fewer data scans than other algorithms, such as k-means and CLARANS, which require multiple full scans. Experimental results show Birch completes clustering significantly faster than these other algorithms while achieving comparable or better clustering quality.
Clustering.pptx
Clustering is an unsupervised learning technique that partitions data into groups called clusters based on similarities between data points. There are two main types of clustering - hard clustering which assigns each data point to one cluster, and soft clustering which assigns probabilities of cluster membership. Hierarchical clustering creates hierarchical clusters where clusters have a nested tree structure, and can be visualized using a dendrogram. K-means clustering is an iterative algorithm that partitions data into a predefined number of clusters K by minimizing distances between data points and cluster centroids.
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What is cluster analysis
- 1. What is Cluster Analysis? • Cluster: a collection of data objects – Similar to one another within the same cluster – Dissimilar to the objects in other clusters • Cluster analysis – Grouping a set of data objects into clusters • Clustering is unsupervised classification: no predefined classes • Typical applications – As a stand-alone tool to get insight into data distribution – As a preprocessing step for other algorithms
- 2. Examples of Clustering Applications • Marketing: Help marketers discover distinct groups in their customer bases, and then use this knowledge to develop targeted marketing programs • Land use: Identification of areas of similar land use in an earth observation database • Insurance: Identifying groups of motor insurance policy holders with a high average claim cost • City-planning: Identifying groups of houses according to their house type, value, and geographical location • Earth-quake studies: Observed earth quake epicenters should be clustered along continent faults
- 3. What Is Good Clustering? • A good clustering method will produce high quality clusters with – high intra-class similarity – low inter-class similarity • The quality of a clustering result depends on both the similarity measure used by the method and its implementation. • The quality of a clustering method is also measured by its ability to discover some or all of the hidden patterns.
- 4. Requirements of Clustering in Data Mining • Scalability • Ability to deal with different types of attributes • Discovery of clusters with arbitrary shape • Minimal requirements for domain knowledge to determine input parameters • Able to deal with noise and outliers • Insensitive to order of input records • High dimensionality • Incorporation of user-specified constraints • Interpretability and usability
- 5. Data Structures • Data matrix – (two modes) • Dissimilarity matrix – (one mode) npx...nfx...n1x ............... ipx...ifx...i1x ............... 1px...1fx...11x 0...)2,()1,( ::: )2,3() ...ndnd 0dd(3,1 0d(2,1) 0
- 6. Measure the Quality of Clustering • Dissimilarity/Similarity metric: Similarity is expressed in terms of a distance function, which is typically metric: d(i, j) • There is a separate “quality” function that measures the “goodness” of a cluster. • The definitions of distance functions are usually very different for interval-scaled, boolean, categorical, ordinal and ratio variables. • Weights should be associated with different variables based on applications and data semantics. • It is hard to define “similar enough” or “good enough” – the answer is typically highly subjective.
- 7. Measure the Quality of Clustering • Dissimilarity/Similarity metric: Similarity is expressed in terms of a distance function, which is typically metric: d(i, j) • There is a separate “quality” function that measures the “goodness” of a cluster. • The definitions of distance functions are usually very different for interval-scaled, boolean, categorical, ordinal and ratio variables. • Weights should be associated with different variables based on applications and data semantics. • It is hard to define “similar enough” or “good enough” – the answer is typically highly subjective.