Paper Info
Title
Online fuzzy medoid based clustering algorithms
Abstract
This paper describes two new online fuzzy clustering algorithms based on medoids. These algorithms have been developed to deal with either very large datasets that do not fit in main memory or data streams in which data are produced continuously. The innovative aspect of our approach is the combination of fuzzy methods, which are well adapted to outliers and overlapping clusters, with medoids and the introduction of a decay mechanism to adapt more effectively to changes over time in the data streams. The use of medoids instead of means allows to deal with non-numerical data (e.g. sequences...) and improves the interpretability of the cluster centers. Experiments conducted on artificial and real datasets show that our new algorithms are competitive with state-of-the-art clustering algorithms in terms of purity of the partition, F1 score and computation times. Finally, experiments conducted on artificial data streams show the benefit of our decay mechanism in the case of evolving distributions.
Year
DOI
Venue
2014
10.1016/j.neucom.2012.07.057
Neurocomputing
Keywords
Field
DocType
decay mechanism,artificial data stream,fuzzy method,new online fuzzy clustering,online fuzzy medoid,large datasets,non-numerical data,real datasets,new algorithm,state-of-the-art clustering algorithm,data stream,medoids,fuzzy clustering,stream
Data mining,Fuzzy clustering,Data stream mining,Computer science,Artificial intelligence,Cluster analysis,Medoid,Interpretability,F1 score,Pattern recognition,Fuzzy logic,Outlier,Machine learning
Journal
Volume
ISSN
Citations 
126,
0925-2312
7
PageRank 
References 
Authors
0.45
36
1
Name
Order
Citations
PageRank
Nicolas Labroche113917.87