Evaluating a clustering solution: An application in the tourism market

1. Evaluating a clustering solution: An application in the tourism market Advisor: Dr. Hsu Graduate: Yung-Chu Lin IDS Lab Seminar

2. Outline • Motivation • Objective • The various paradigms • The number of clusters • Utility concepts • Proposed approach • A tourism market application • Conclusion IDS Lab Seminar

3. Motivation • To evaluate a clustering solution IDS Lab Seminar

4. Objective • Propose a framework for evaluating a clustering solution • Advocate a multimethodological approach IDS Lab Seminar

5. The various paradigms • Statistical method • Measures of association, association test, Automatic Interaction Detection(AID), Classification and Regression Tree-CART, Discriminant Analysis and Logistic Regression • Machine Learning • Tree Classification algorithm-C4.5 and prepositional rules-CN2 • The conjugation of methodologies sets the stage for dealing with rich and complex problems IDS Lab Seminar

6. Statistical methodologies • Association between two nominal variables • Cramer Statistic IDS Lab Seminar

7. Statistical methodologies(cont’d) • Uncertainty Coefficient IDS Lab Seminar

8. Statistical methodologies(cont’d) • Mutual Information • ANOVA • MANOVA • CART • Discriminant Analysis • Logistic Regression IDS Lab Seminar

9. Machine learning methodologies • Decision Trees • Provide a hierarchical process and model of classification • Nonbacktracking and greedy optimisation algorithm • Propositional Rules • Provide logic models • Represented by “if condition then cluster” • Neural Networks • Navie Bayes IDS Lab Seminar

10. The number of clusters • May be set a priori • May be an outcome of the clustering process itself • The best number is obtained by comparing measures of model fit for as alternative numbers of clusters IDS Lab Seminar

11. The number of clusters(cont’d) • Mixture Model • Akaike Criteria(AIC) IDS Lab Seminar

12. Utility concepts • The main question in evaluating a clustering  a question about utility • Utility is evaluated by judgement IDS Lab Seminar

13. preprocess Proposed approach IDS Lab Seminar

14. Proposed approach(cont’d) • The choice of a discriminant and classification methodologies  the nature of variables • Regarding discrimination, complementary dimensions offer a new perspective and understanding • An integration of methodologies and techniques based on the Statistical and Machine Learning Paradigms IDS Lab Seminar

15. A tourism market application • The clustering solution • Evaluation of clustering solution IDS Lab Seminar

16. Data base • The answers to a questionnaire: Portuguese clients of Pousadas de Portugal • 49 questions  200 variables • 2500 Portuguese clients IDS Lab Seminar

17. Clustering • Model sample: 1647 clients (65%)； Validation sample: 897 clients (35%) • Use a priori and a K-Means procedure • 4 variables expressing the frequency and type of Pousadas • CH, CSUP, C and B type • 3 clusters (First time user, Regular users and Heavy users) • Model: 18%, 60% and 22% • Validation: 16%, 62% and 22% IDS Lab Seminar

18. Clustering(cont’d) • 2 clusters (Heavy users and Regular users) • Model: 16 Pousadas and 5 Pousadas • Validation: 17 Pousadas and 4 Pousadas IDS Lab Seminar

19. A tourism market application • The clustering solution • Evaluation of clustering solution IDS Lab Seminar

20. Evaluation of clustering solution IDS Lab Seminar

21. Analysis of association between clusters and clustering base • Measure the degree of correction in classification • Model: 82.6% ；Validation: 91.5% • The linear combinations of the clustering base variables that maximise the ratio between-within cluster variation IDS Lab Seminar

22. Analysis of association between clusters and clustering base(cont’d) IDS Lab Seminar

23. Analysis of association between clusters and other variables • Chi-square the strength of association between clusters and variables • Rule Induction Procedures discriminate and classify on the base of attributes significantly associated with clusters • Rule induction provide a better comprehension of the facts discriminating the clusters • C4.5 and CN2 evaluate both Model sample and Validation sample IDS Lab Seminar

24. Analysis of association between clusters and other variables(cont’d) • Memorize a group/beam of the best solutions IDS Lab Seminar

25. Analysis of association between clusters and other variables(cont’d) IDS Lab Seminar

26. Analysis of association between clusters and other variables(cont’d) IDS Lab Seminar

27. Analysis of association between clusters and other variables(cont’d) IDS Lab Seminar

28. Global evaluation • In Discriminant Analysis and Logistic Regressionclearly the differences between clusters • Chi-square tests association between variables and clusters • C4.5 and CN2 provides a more complex and richer perspective IDS Lab Seminar

29. Conclusion • Identifying significant associations characterising the clustered entities guided discriminant and classification analysis • Propositional rule induction is suitable for discriminating purposes • Multimethodological approach should consider not only inference but also descriptive analysis IDS Lab Seminar