Cluster analysis using spss
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This document provides an overview and agenda for a presentation on multivariate analysis. It introduces the presenter, Dr. Nisha Arora, and lists her qualifications and areas of expertise, which include statistics, data analysis, machine learning, and online teaching. The presentation agenda covers topics like cluster analysis using SPSS, including different clustering algorithms, applications of cluster analysis, and how to interpret and validate clustering outputs and solutions.
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Cluster analysis using spss
- 1. MULTIVARIATE ANALYSIS - Dr Nisha Arora
- 2. About Me Concepts How it Works? Q/A Session Agenda
- 3. • Dr. Nisha Arora is a proficient educator, passionate trainer, You Tuber, occasional writer, and a learner forever. ✓ PhD in Mathematics. ✓ Works in the area of Data Science, Statistical Research, Data Visualization & Storytelling ✓ Creator of various courses ✓ Contributor to various research communities and Q/A forums ✓ Mentor for women in Tech Global 3 About Me An educator by heart & a trainer by profession.
- 4. http://stats.stackexchange.com/users/79100/learner https://stackoverflow.com/users/5114585/dr-nisha-arora https://www.quora.com/profile/Nisha-Arora-9 https://www.researchgate.net/profile/Nisha_Arora2/contributions http://learnerworld.tumblr.com/ https://www.slideshare.net/NishaArora1 https://scholar.google.com/citations?user=JgCRWh4AAAAJ&hl=en&authuser= 1 https://www.youtube.com/channel/UCniyhvrD_8AM2jXki3eEErw https://groups.google.com/g/dataanalysistraining/search?q=nisha%20arora https://www.linkedin.com/in/drnishaarora/detail/recent-activity/posts/ ✓ Research Queries ✓ Coding Queries ✓ Blog Posts ✓ Slide Decks ✓ My Talks ✓ Publications ✓ Lectures ✓ Layman’s Term Explanation ✓ Mentoring ✓ Articles & Much More My Contribution to the Community
- 5. ❖ Statistics ❖ Data Analysis ❖ Machine Learning ❖ Analytics & Data Science ❖ Data Visualization & Storytelling ❖ Mathematics & Operations Research ❖ Online Teaching ❖ Excel/SPSS/R/Python/Shiny ❖ Tableau/PowerBI My Expertise
- 8. Applications ✓ Clusters of covid active cases ✓ Assign projects to different teams of students where each team member have similar interest ✓ Customer segmentation ✓ Market Basket Analysis
- 9. Clustering Evaluations ✓ Within group variation should be less ✓ Between group variation should be more
- 11. Clustering Algorithms Clustering Techniques Hierarchical Divisive Agglomerative Partitional Centroid Model Based Graph Theoretic Spectral Bayesian Decision Based Non- parametric
- 13. Available Options Analyze -> Classify -> ✓ Hierarchical cluster ✓ K-means cluster ✓ TwoStep cluster ✓ Cluster Silhouttes
- 18. Hierarchical clustering_ Outputs Proximity Matrix It gives the distances or similarities between items. ✓ Double Click ✓ Pivot
- 19. Hierarchical clustering_ Outputs Agglomeration schedule It displays the cases or clusters combined at each stage, the distances between the cases or clusters being combined, and the last cluster level at which a case (or variable) joined the cluster.
- 20. Hierarchical clustering_ Outputs Icicle ✓ It displays an icicle plot, including all clusters or a specified range of clusters. ✓ It displays information about how cases are combined into clusters at each iteration of the analysis.
- 21. Hierarchical clustering_ Outputs Icicle ✓ Double Click ✓ Options ✓ Y axis reference line ✓ Position – 10 ✓ Apply
- 24. Hierarchical clustering_ Outputs Dendrograms can be used to assess the cohesiveness of the clusters formed and can provide information about the appropriate number of clusters to keep. Possible Clusters – 2/3/6/… Cluster Sizes ?
- 25. Hierarchical clustering Let’s change the number of possible solutions
- 26. Hierarchical clustering _ Output We get additional output as cluster membership
- 27. Hierarchical clustering Let’s change the icicles for specified range of clusters
- 28. Hierarchical clustering _ Output Let’s change the icicles for specified range of clusters
- 29. Hierarchical clustering_ Outputs ✓ Cluster Membership ✓ We can save cluster memberships for a single solution or a range of solutions. ✓ Saved variables can then be used in subsequent analyses to explore other differences between groups.
- 30. Understanding the clusters Cross Tab between rank and cluster membership We need to give suitable names to the clusters.
- 31. Understanding the clusters We need to give suitable names to the clusters. We can do it in variable view Let’s give the names: Cluster 1: Seniors Cluster 3: Adjuncts Cluster 2: Others
- 32. Understanding the clusters We need to give suitable names to the clusters. We can do it in variable view Let’s give the names: Cluster 1: Seniors Cluster 3: Adjuncts Cluster 2: Others
- 34. Understanding the clustering Although cell count is too low & chi- square statistics is not reliable, still we see there’s no association between sex & cluster membership prima facie.
- 35. Validating Hierarchical Clustering Double click ‘Agglomerative Schedule’ table → Select ‘Coefficients’ → Right click → Create Graph → Line Look at the plot (like scree plot in factor analysis) → Elbow should be formed Find stage number where elbow is formed Number of clusters = Total cases – stage number where elbow is formed
- 36. K-means clustering 1. Need to predefine the number of cluster 2. Solution depends on initial cluster center 3. Not all patterns can be segmented 4. Bases on Euclidean distance 1. Fast (Linear time complexity) 2. Easy to understand 3. Most popular
- 37. K-means clustering Number of Cluster: Ideally between 2 to 5 [Subjective] Number of iteration: 10/20 should be enough
- 38. K-means clustering We can save cluster membership.
- 39. K-means clustering In ‘Statistics’ sub- dialog box: Initial cluster center: Randomly chosen
- 44. K-means clustering _Output We get almost similar cluster membership Actually, we should first standardize scores Also, k-means works on Euclidean distance
- 45. To validate K-means clustering Analyze → Compare Means → Take all variables used for clustering in ‘Dependent List’ And cluster membership in ‘Factor’ → Run ‘Bonferroni or Tukey post hoc test → See if all p-values are less than level of significant (0.05)
- 46. How to standardize variables Analyze → Select variables → Check ‘Save standardized values as variables’ → Click ‘OK’
- 47. How to convert string variables to categorical Transform → Automatic Recode → Double-click variable State in the left column to move it to the Variable → ‘New Name box’: Enter a name for the new, recoded variable in the New Name field → click ‘Add New Name’ Check the box for Treat blank string values as user-missing. Click OK to finish
- 48. How to add ID column to data Transform → Compute Variable → Give a name to ‘Target variable, say, ‘ID’→ Type ‘$CASENUM’ in Numeric Expression box (Or double click on $Casenum function from Functions & Special Variables menu) → click ‘OK’
- 49. Thank You