Paper Info
Title
Large-Scale Experimental Evaluation of Cluster Representations for Multiobjective Evolutionary Clustering
Abstract
Multiobjective evolutionary clustering algorithms are based on the optimization of several objective functions that guide the search following a cycle based on evolutionary algorithms. Their capabilities allow them to find better solutions than with conventional clustering algorithms if the suitable individual representation is selected. This paper provides a detailed analysis of the three most relevant and useful representations-prototype-based, label-based, and graph-based-through a wide set of synthetic data sets. Moreover, they are also compared to relevant conventional clustering algorithms. Experiments show that multiobjective evolutionary clustering is competitive with regard to other clustering algorithms. Furthermore, the best scenario for each representation is also presented.
Year
DOI
Venue
2014
10.1109/TEVC.2013.2281513
Evolutionary Computation, IEEE Transactions  
Keywords
Field
DocType
data mining,data structures,evolutionary computation,pattern clustering,cluster representations,evolutionary algorithms,graph-based representation,label-based representation,multiobjective evolutionary clustering algorithms,objective functions,prototype-based representation,synthetic data sets,Clustering,data mining,multiobjective evolutionary algorithms
Data mining,Fuzzy clustering,CURE data clustering algorithm,Artificial intelligence,Biclustering,Cluster analysis,Canopy clustering algorithm,Mathematical optimization,Clustering high-dimensional data,Correlation clustering,Constrained clustering,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
18
1
1089-778X
Citations 
PageRank 
References 
17
0.64
23
Authors
7
Name
Order
Citations
PageRank
Alvaro Garcia-Piquer1383.19
Albert Fornells21189.27
Jaume Bacardit3109147.21
Albert Orriols-Puig451125.91
Elisabet Golobardes520620.16
Garcia-Piquer, A.6170.64
Orriols-Puig, A.7221.25