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Program_Cluster_Analysis
- 1. PROGRAM: CLUSTER ANALYSISWITH R MODULE : K-MEANS AND HIERARCHICAL CLUSTERING BY SAMMYA SENGUPTA Cluster Analysis Workshop
- 2. WHAT IS CLUSTERANALYSIS Cluster analysis is a class of techniques used to classify objects or cases into relatively homogeneous groups called cluster. Objects of each cluster tend to be similar to each other and dissimilar to objects in other clusters. Cluster analysis is called classification analysis or numerical taxonomy.
- 3. HOW CLUSTERS BEINGFORMED IN K-MEANS METHOD
- 4. USAGE OF CLUSTERANALYSIS IN BUSINESS CONTEXT Segmenting the market Understanding buyer behavior Indentifying new prod. Development Selecting test markets
- 5. CLUSTER ANALYSIS ASA MULTIVARIATE TECHNIQUE Cluster analysis classifies objects(e.g., respondents, products etc) so that each objects is similar to others in the cluster based on a set of selected characteristics. The resulting clusters of objects should exhibit high internal(within cluster) homogeneity and high external(between cluster) heterogeneity.
- 6. SELECTION OF CLUSTERINGVARIABLES Conceptual Consideration Practical Consideration
- 7. NECESSITY OF CONCEPTUALSUPPORT IN CLUSTER ANALYSIS (LITTLE BIT CAUTION…..) Cluster analysis is descriptive, atheoretical and non-inferential Cluster analysis will always create clusters, regardless of the actual existence of any structure in the data The cluster solutions is not generalizable because it is totally dependent upon the variables used as the basis for the similarity measure
- 8. RESEARCH DESIGN OFCLUSTER ANALYSIS Is the sample size is adequate Can outlier be detected, if so, should they be deleted How should object similarity measure Should data be standardized
- 9. MEASURING SIMILARITY Correlationmeasure Distance measure
- 10. DISTANCE MEASURE Euclideandistance Squared Euclidean distance City-block Manhattan distance Chebychev distance Mahalanblish distance
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- 12. WHY DATA STANDARDIZATIONIS REQUIRED Mean of the Standardized data is 0 and S.D is 1
- 13. ASSUMPTIONS IN CLUSTERANALYSIS Impact of Multicollinearity
- 14. DERIVING CLUSTERS ANDASSESSING OVERALL MODEL FIT Select the partitioning procedure used for forming clusters Make decisions on the number of clusters to be formed
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- 16. HIERARCHICAL CLUSTERING Hierarchicalprocedure involves a series of n- 1 clustering decisions(where n equals the no of Obs.) that combine into a hierarchy or a tree like structure. Two basic types of hierarchical clustering procedure are Agglomerative and Divisive.
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- 20. K-MEANS CLUSTERING Incontrast to hierarchical methods, non hierarchical procedure do not involve the tree like construction process. Instead they assign objects into clusters once the number of clusters to be formed is specified.
- 21. SPECIFY CLUSTER SEEDS Selecting seed points - Researcher Specified - Sample Generated
- 22. K-MEANS CLUSTERING ALGORITHM Sequential threshold Parallel threshold optimization
- 23. NUMBER OF CLUSTERSTO BE FORMED Measures of Heterogeneity change Percentage change in heterogeneity Measures of Variance change(RMSSTD) Statistical Measure of heterogeneity change Direct Measures of Heterogeneity N.B- A rule of thumb in the practical field is that 4-6 cluster will be a perfect fit.
- 24. SHOULD HIERARCHICAL ORNON- HIERARCHICAL PROCEDURE USED Hierarchical Clustering is not suitable for large datasets as the multitude of calculations involved would be impossibly huge. Thus K-Means clustering is the most used method of clustering.
- 25. CLUSTER PROFILING(K-MEANS) ClusterSolution is profiled against Variables to identify and assign the character of individual clusters.
- 26. SEGMENTATION EXTREMELY VALUE SEEKER 11% EXTREMELY DISCOUNT MINDED 12% DISCOUNT ORIENTEDBUT ALSO VALUE SEEKER 11% VALUE SEEKER BUT DON'T LET DISCOUNT 16% MIXED ORIANTATION 35% VALUE SEEKER 15% SEGMENTATION
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