Dual clustering ： integrating data clustering over optimization and constraint domains

1. Dual clustering：integrating data clustering over optimization and constraint domains Advisor : Dr. Hsu Presenter : Wen-Cheng Tsai Author : Cheng-Ru Lin, Ken-Hao Liu Ming-Syan Chen, TKDE,2005

2. Outline • Motivation • Objective • Method • Experience • Conclusion • Personal Comments

3. Motivation • In the conventional spatial clustering , the input data set is partitioned into several compact regions and data point which are similar to another in their nongeometric attributes may be scattered over different region, thus making the corresponding objective difficult to achieve.

4. Objective • Our goal is to optimize the objective function in the optimization domain while satisfying the constraint specified in the constraint domain. • We devise an effective algorithm, named Interlaced Clustering-Classification (ICC), to solve this problem.

5. Method Projection on the constraint domain Projection on the optimization domain ICC Algorithm Data Set 1 Step 1 Step 4 Result of ICC , k=5 , ω=0.7 , l=5 Step 6

6. Data Set1 Experience Data Set 2 Projection on the constraint domain Projection on the optimization domain Result of ICC with ω=0.7 Result of ICC with ω=0.9 Result of CR with p=3 Result of CR with p=1/3 Result of KNNC with k=5 Result of KNNC with k=30

7. Conclusion • ICC algorithm combines the information in both domains and iteratively performs a clustering algorithm on the optimization domain and also a classification algorithm on the constraint domain to reach the target clustering effectively.

8. Personal Comments • Advantages • reach the target clustering effectively on both domains • deal with both domains at the same time • Disadvantage …