presenter : jhou, yu-liang authors : yiu-ming cheung, hong jia 2013,pr

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


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


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.


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.


Methodologyobject-cluster similarity metric

categorical attribute


Methodologyobject-cluster similarity metric

• numerical attributes

• mixed data


MethodologyIterative clustering algorithm


MethodologyAutomatic selection of cluster number

Competition mechanism


MethodologyAutomatic selection of cluster number

Penalized mechanism


Experiments-data sets


Experiments mixed data


Experiments categorical data


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

consuming and efficiency of the process and

overcome the cluster number selection problem.


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

presenter : jhou, yu-liang authors : yiu-ming cheung, hong jia 2013,pr

Documents

numerical data

mixed attributes

similarity metrics

unied similarity metric

pr categorical

general clustering framework

uniedsimilarity metric

yiuming cheung