Presenter : Kung, Chien-Hao Authors : Eghbal G. Mansoori 2011,IEEE

Intelligent Database Systems Lab

Presenter : Kung, Chien-Hao

Authors : Eghbal G. Mansoori

2011,IEEE

FRBC: A Fuzzy Rule-Based Clustering Algorithm


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


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.


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.


Methodology-Fuzzy • Fuzzification

• Fuzzy Rule

• Fuzzy Inference Mechanism

• Defuzzifierion


MethodologyGenerate auxiliary data

Choose the best rule

Clustering

Regroup remained data




Clustering





Clustering





Clustering





Clustering



Experiment


Experiment


Experiment


Experiment

T=0.1 T=0.01


Experiment


Experiment


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.


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.

Documents

Presenter : Kung, Chien-Hao Authors : Eghbal G. Mansoori 2011,IEEE