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

Intelligent Database Systems Lab

OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments

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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.

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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.

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Methodology-Fuzzy • Fuzzification

• Fuzzy Rule

• Fuzzy Inference Mechanism

• Defuzzifierion

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MethodologyGenerate auxiliary data

Choose the best rule

Clustering

Regroup remained data

Intelligent Database Systems Lab

MethodologyGenerate auxiliary data

Choose the best rule

Clustering

Regroup remained data

Intelligent Database Systems Lab

MethodologyGenerate auxiliary data

Choose the best rule

Clustering

Regroup remained data

Intelligent Database Systems Lab

MethodologyGenerate auxiliary data

Choose the best rule

Clustering

Regroup remained data

Intelligent Database Systems Lab

MethodologyGenerate auxiliary data

Choose the best rule

Clustering

Regroup remained data

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Experiment

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Experiment

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Experiment

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Experiment

T=0.1 T=0.01

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Experiment

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Experiment

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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.

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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.