a link-based cluster ensemble approach for categorical data clustering
DESCRIPTION
A Link-Based Cluster Ensemble Approach for Categorical Data Clustering. Presenter : Jian-Ren Chen Authors : Natthakan Iam -On, Tossapon Boongoen , Simon Garrett, and Chris Price 2012 , IEEE. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering](https://reader036.vdocuments.us/reader036/viewer/2022062520/568161c9550346895dd1af26/html5/thumbnails/1.jpg)
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
![Page 2: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering](https://reader036.vdocuments.us/reader036/viewer/2022062520/568161c9550346895dd1af26/html5/thumbnails/2.jpg)
Intelligent Database Systems Lab
OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments
![Page 3: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering](https://reader036.vdocuments.us/reader036/viewer/2022062520/568161c9550346895dd1af26/html5/thumbnails/3.jpg)
Intelligent Database Systems Lab
Motivation• Cluster Ensembles:
combine different clustering decisions in such a
way as to achieve accuracy superior to that of
any individual clustering.
![Page 4: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering](https://reader036.vdocuments.us/reader036/viewer/2022062520/568161c9550346895dd1af26/html5/thumbnails/4.jpg)
Intelligent Database Systems Lab
Objectives• A new link-based approach improves the conventional
matrix by discovering unknown entries through
similarity between clusters in an ensemble.
![Page 5: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering](https://reader036.vdocuments.us/reader036/viewer/2022062520/568161c9550346895dd1af26/html5/thumbnails/5.jpg)
Intelligent Database Systems Lab
Methodology
Creating a Cluster Ensemble
Generating a Refined Matrix
Applying a Consensus Function to RM
![Page 6: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering](https://reader036.vdocuments.us/reader036/viewer/2022062520/568161c9550346895dd1af26/html5/thumbnails/6.jpg)
Intelligent Database Systems Lab
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)
![Page 7: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering](https://reader036.vdocuments.us/reader036/viewer/2022062520/568161c9550346895dd1af26/html5/thumbnails/7.jpg)
Intelligent Database Systems Lab
MethodologyCreating a Cluster
Ensemble
Generating a Refined Matrix
Applying a Consensus Function to RM
![Page 8: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering](https://reader036.vdocuments.us/reader036/viewer/2022062520/568161c9550346895dd1af26/html5/thumbnails/8.jpg)
Intelligent Database Systems Lab
MethodologyCreating a Cluster
Ensemble
Generating a Refined Matrix
Applying a Consensus Function to RM
![Page 9: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering](https://reader036.vdocuments.us/reader036/viewer/2022062520/568161c9550346895dd1af26/html5/thumbnails/9.jpg)
Intelligent Database Systems Lab
Methodology
• given a graph G = (V,W)• SPEC finds the K largest eigenvectors
of W• formed another matrix U
Creating a Cluster Ensemble
Generating a Refined Matrix
Applying a Consensus Function to RM
![Page 10: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering](https://reader036.vdocuments.us/reader036/viewer/2022062520/568161c9550346895dd1af26/html5/thumbnails/10.jpg)
Intelligent Database Systems Lab
Experiments
• Investigated Data Sets
![Page 11: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering](https://reader036.vdocuments.us/reader036/viewer/2022062520/568161c9550346895dd1af26/html5/thumbnails/11.jpg)
Intelligent Database Systems Lab
Experiments
![Page 12: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering](https://reader036.vdocuments.us/reader036/viewer/2022062520/568161c9550346895dd1af26/html5/thumbnails/12.jpg)
Intelligent Database Systems Lab
Experiments
![Page 13: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering](https://reader036.vdocuments.us/reader036/viewer/2022062520/568161c9550346895dd1af26/html5/thumbnails/13.jpg)
Intelligent Database Systems Lab
Experiments
![Page 14: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering](https://reader036.vdocuments.us/reader036/viewer/2022062520/568161c9550346895dd1af26/html5/thumbnails/14.jpg)
Intelligent Database Systems Lab
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.
![Page 15: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering](https://reader036.vdocuments.us/reader036/viewer/2022062520/568161c9550346895dd1af26/html5/thumbnails/15.jpg)
Intelligent Database Systems Lab
Comments• Advantages– The link-based method is efficient.
• Applications– Categorical Data Clustering