Unsupervised Evolutionary Clustering Algorithm for Mixed Type Data

1. Unsupervised Evolutionary Clustering Algorithm for Mixed Type Data ZhiZheng, Maoguo Gong , Jingjing Ma , Licheng Jiao , Qiaodi Wu 2010,CEC Presented by Chien-Hao Kung 2011/12/1

2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

3. Motivation • As a partitional clustering algorithm, K-prototype (KP) algorithm is a well-known one for mixed type data. • However, it is sensitive to initialization and converges to local optimum easily.

4. Objectives In this study, KP is applied as a local search strategy, and runs under the Global searching to help KP overcome its flaws.

5. Methodology • K-prototype Algorithm • Step1.Initializing. • Step2.For each data item, calculating the distances. • Step3.Retest every data item. • Step4.Repeat Step3. until no item changes its cluster.

6. Methodology • Evolutionary k-prototype(EKP) • Step1 Initialization. • Step2 Crossover. • Step3 Mutation. • Step4 KP Search. • Step5 Evaluation and Selection. • Step6 Termination Test.

7. Methodology • Initialization • There are 8 parameters have to be set before evolution. • Cluster number • r is a weight in EKP which balance the influence on clustering • Population size • Proportion of initial individuals that generated by choosing items randomly in dataset (IP) • Crossover probability • Mutation probability • in simulated binary crossover(SBX) • n in polynomial mutation

8. Methodology • Initialization • Two kinds of random initialization schemes • The first is randomly choosing K data item as the prototypes of clusters • The second is randomly generating K prototypes • Ex: • [2.23,5.63],[6.56,5.13], and {1,2,3,4,5,6},{2,4} • =>{3.21,6.23,2,4}

9. Methodology • Crossover. • Numerical type --Simulated binary crossover(SBX) • Categorical type – Single point crossover

10. Methodology Mutation

11. Methodology • KP Search • Evaluation and Selection • Termination Test

12. Experiments Parameter setting

13. Experiments

14. Experiments

15. Experiments • Dataset

16. Experiments

17. Conclusions • This paper propose a novel unsupervised clustering algorithm for mixed type data named evolutionary k-prototype(EKP) . • The experiment result show that the evolutionary framework improves the original algorithms markedly. • EKP which can adjust this weight automatically needs to be studied.

18. Comments • Drawback • This method use the parameter too much. Application • Clustering