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Author Abdullin, A. ♦ Nasraoui, O.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2012
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Peer to peer computing ♦ heterogeneous data set ♦ Clustering algorithms ♦ Collaboration ♦ Hidden Markov models ♦ Linear programming ♦ clustering ♦ Partitioning algorithms ♦ Distortion measurement
Abstract Recent years have seen an increasing interest in clustering data comprising multiple domains or modalities, such as categorical, numerical and transactional, etc. This kind of data is sometimes found within the context of clustering multiview, heterogeneous, or multimodal data. Traditionally, different types of attributes or domains have been handled by first combining them into one format (possibly using some type of conversion) and then following with a traditional clustering algorithm, or computing a combined distance matrix that takes into account the distance values for each domain, then following with a relational or graph clustering approach. In other cases where data consists of multiple views, multiview clustering has been used to cluster the data. In this paper, we review the existing approaches such as multiview clustering and discuss several additional approaches that can be harnessed for the purpose of clustering heterogeneous data once they are adapted for this purpose. The additional approaches include ensemble clustering, collaborative clustering and semi-supervised clustering.
ISBN 9781467344739
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2012-10-25
Publisher Place Colombia
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 1.96 MB
Page Count 8
Starting Page 1
Ending Page 8


Source: IEEE Xplore Digital Library