Paper Info
Title
A Clustering Ensemble Method For Clustering Mixed Data
Abstract
This paper presents a clustering ensemble method based on our novel three-staged clustering algorithm. A clustering ensemble is a paradigm that seeks to best combine the outputs of several clustering algorithms with a decision fusion function to achieve a more accurate and stable final output. Our ensemble is constructed with our proposed clustering algorithm as a core modelling method that is used to generate a series of clustering results with different conditions for a given dataset. Then, a decision aggregation mechanism such as voting is employed to find a combined partition of the different clusters. The voting mechanism considered only experimental results that produce intra-similarity value higher than the average intra-similarity value for a particular interval. The aim of this procedure is to find a clustering result that minimizes the number of disagreements between different clustering results. Our ensemble method has been tested on 11 benchmark datasets and compared with some individual methods including TwoStep, k-means, squeezer, k-prototype and some ensemble based methods including k-ANMI, ccdByEnsemble, SIPR, and SICM. The experimental results showed its strengths over the compared clustering algorithms.
Year
DOI
Venue
2010
10.1109/IJCNN.2010.5596684
2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010
Keywords
Field
DocType
cancer,k means,voting,accuracy,mathematical model,clustering algorithms,data handling
Fuzzy clustering,Data mining,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,Single-linkage clustering,Canopy clustering algorithm,Data stream clustering,Correlation clustering,Pattern recognition,Determining the number of clusters in a data set,Machine learning
Conference
ISSN
Citations 
PageRank 
2161-4393
3
0.47
References 
Authors
14
2
Name
Order
Citations
PageRank
Jamil Alshaqsi130.47
Wenjia Wang2579.12