a link-based cluster ensemble approach for categorical data clustering

Intelligent Database Systems Lab

Presenter : JIAN-REN CHEN

Authors : Natthakan Iam-On, Tossapon Boongoen,

　　 Simon Garrett, and Chris Price

2012 , IEEE

A Link-Based Cluster Ensemble Approachfor Categorical Data Clustering


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


Motivation• Cluster Ensembles:

combine different clustering decisions in such a

way as to achieve accuracy superior to that of

any individual clustering.


Objectives• A new link-based approach improves the conventional

matrix by discovering unknown entries through

similarity between clusters in an ensemble.


Methodology

Creating a Cluster Ensemble

Generating a Refined Matrix

Applying a Consensus Function to RM





MethodologyType I (Direct ensemble):

Type II (Full-space ensemble)

Type III (Subspace ensemble)


MethodologyCreating a Cluster

Ensemble




Methodology

• given a graph G = (V,W)• SPEC finds the K largest eigenvectors

of W• formed another matrix U





Experiments

• Investigated Data Sets


Experiments


Conclusions• Constructing the RM is efficiently resolved by the

similarity among categorical labels, using the

Weighted Triple-Quality similarity algorithm.

• The link-based method usually achieves superior

clustering results.


Comments• Advantages– The link-based method is efficient.

• Applications– Categorical Data Clustering

a link-based cluster ensemble approach for categorical data clustering

Documents

cluster ensemblegenerating

consensus function

refined matrixapplying

space ensemble type

superior clustering

individual clustering

different clustering

matrix ucreating