Intelligent Database Systems Lab

Presenter : JHOU, YU-LIANG

Authors : Yiu-ming Cheung, Hong Jia

2013,PR

Categorical-and-numerical-attribute data clustering based on a unified

similarity metric without knowing cluster number

Intelligent Database Systems Lab

OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments

Intelligent Database Systems Lab

Motivation• It is a nontrivial task to perform clustering on

mixed data because there exists an awkward

gap between the similarity metrics for

categorical and numerical data.

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Objectives• This paper presents a general clustering framework

based on the concept of object-cluster similarity and

gives a unified similarity metric which can be applied

to the data with categorical, numerical, and mixed

attributes.

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Methodologyobject-cluster similarity metric

categorical attribute

Intelligent Database Systems Lab

Methodologyobject-cluster similarity metric

• numerical attributes

• mixed data

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MethodologyIterative clustering algorithm

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MethodologyAutomatic selection of cluster number

Competition mechanism

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MethodologyAutomatic selection of cluster number

Penalized mechanism

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Experiments-data sets

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Experiments mixed data

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Experiments categorical data

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Conclusions• We adopt our new approach can improve the time-

consuming and efficiency of the process and

overcome the cluster number selection problem.

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Comments• Advantages More save time and efficiency .Applications-Clustering