loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Elnaz Bigdeli 1 ; Mahdi Mohammadi 2 ; Bijan Raahemi 2 and Stan Matwin 3

Affiliations: 1 Ottawa University, Canada ; 2 University of Ottawa, Canada ; 3 Dalhousie, Canada

ISBN: 978-989-758-048-2

Keyword(s): Density-based Clustering, Cluster Summarization, Gaussian Mixture Model.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Clustering and Classification Methods ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Pre-Processing and Post-Processing for Data Mining ; Soft Computing ; Symbolic Systems

Abstract: One of the main concerns in the area of arbitrary shape clustering is how to summarize clusters. An accurate representation of clusters with arbitrary shapes is to characterize a cluster with all its members. However, this approach is neither practical nor efficient. In many applications such as stream data mining, preserving all samples for a long period of time in presence of thousands of incoming samples is not practical. Moreover, in the absence of labelled data, clusters are representative of each class, and in case of arbitrary shape clusters, finding the closest cluster to a new incoming sample using all objects of clusters is not accurate and efficient. In this paper, we present a new algorithm to summarize arbitrary shape clusters. Our proposed method, called SGMM, summarizes a cluster using a set of objects as core objects, then represents each cluster with corresponding Gaussian Mixture Model (GMM). Using GMM, the closest cluster to the new test sample is identified with lo w computational cost. We compared the proposed method with ABACUS, a well-known algorithm, in terms of time, space and accuracy for both categorization and summarization purposes. The experimental results confirm that the proposed method outperforms ABACUS on various datasets including syntactic and real datasets. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.175.174.157

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Bigdeli, E.; Bigdeli, E.; Mohammadi, M.; Raahemi, B. and Matwin, S. (2014). Arbitrary Shape Cluster Summarization with Gaussian Mixture Model.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, pages 43-52. DOI: 10.5220/0005071500430052

@conference{kdir14,
author={Elnaz Bigdeli. and Elnaz Bigdeli. and Mahdi Mohammadi. and Bijan Raahemi. and Stan Matwin.},
title={Arbitrary Shape Cluster Summarization with Gaussian Mixture Model},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={43-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005071500430052},
isbn={978-989-758-048-2},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - Arbitrary Shape Cluster Summarization with Gaussian Mixture Model
SN - 978-989-758-048-2
AU - Bigdeli, E.
AU - Bigdeli, E.
AU - Mohammadi, M.
AU - Raahemi, B.
AU - Matwin, S.
PY - 2014
SP - 43
EP - 52
DO - 10.5220/0005071500430052

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.