1. Intelligent Database Systems Lab Presenter : Kung, Chien-Hao Authors : Eghbal G. Mansoori 2011,IEEE FRBC: A Fuzzy Rule-Based Clustering Algorithm

2. Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

3. Intelligent Database Systems Lab Motivation Clustering response is a primitive exploratory approach in data analysis with little or no prior knowledge. However, the main challenge for most of clustering algorithms is their necessity to know the number of clusters for which to look.

4. Intelligent Database Systems Lab Objectives To overcome these restrictions, a novel fuzzy rule- based clustering algorithm(FRBC) is proposed in this paper. FRBC tries to automatically explore the potential clusters in the data patterns.

5. Intelligent Database Systems Lab Methodology-Fuzzy Fuzzification Fuzzy Rule Fuzzy Inference Mechanism Defuzzifierion

6. Intelligent Database Systems Lab Methodology Generate auxiliary data Choose the best rule Clustering Regroup remained data

7. Intelligent Database Systems Lab Methodology Generate auxiliary data Choose the best rule Clustering Regroup remained data

8. Intelligent Database Systems Lab Methodology Generate auxiliary data Choose the best rule Clustering Regroup remained data

9. Intelligent Database Systems Lab Methodology Generate auxiliary data Choose the best rule Clustering Regroup remained data

10. Intelligent Database Systems Lab Methodology Generate auxiliary data Choose the best rule Clustering Regroup remained data

11. Intelligent Database Systems Lab Experiment

12. Intelligent Database Systems Lab Experiment

13. Intelligent Database Systems Lab Experiment

14. Intelligent Database Systems Lab Experiment T=0.1T=0.01

15. Intelligent Database Systems Lab Experiment

16. Intelligent Database Systems Lab Experiment

17. Intelligent Database Systems Lab Conclusions FRBC is a novel fuzzy rule-based clustering algorithm to automatically explore the potential clusters. The clusters specified by fuzzy rules are human understandable with acceptable accuracy.

18. Intelligent Database Systems Lab Comments Advantages/drawbacks – This paper gives rich experiments for this method – But this method still has a parameter (threshold) to control the number of clusters. Applications – Clustering.