An Extended Affinity Propagation Clustering Method Based on Different Data Density Types

An Extended Affinity Propagation Clustering Method Based on Different Data Density Types

Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points....

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Main Authors: Zhao, XiuLi, Xu, WeiXiang
Format: Online
Language:English
Published: Hindawi Publishing Corporation 2015
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4317584/
id pubmed-4317584
recordtype oai_dc
spelling pubmed-43175842015-02-15 An Extended Affinity Propagation Clustering Method Based on Different Data Density Types Zhao, XiuLi Xu, WeiXiang Research Article Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself. Hindawi Publishing Corporation 2015 2015-01-21 /pmc/articles/PMC4317584/ /pubmed/25685144 http://dx.doi.org/10.1155/2015/828057 Text en Copyright © 2015 X. Zhao and W. Xu. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Zhao, XiuLi
Xu, WeiXiang
spellingShingle Zhao, XiuLi
Xu, WeiXiang
An Extended Affinity Propagation Clustering Method Based on Different Data Density Types
author_facet Zhao, XiuLi
Xu, WeiXiang
author_sort Zhao, XiuLi
title An Extended Affinity Propagation Clustering Method Based on Different Data Density Types
title_short An Extended Affinity Propagation Clustering Method Based on Different Data Density Types
title_full An Extended Affinity Propagation Clustering Method Based on Different Data Density Types
title_fullStr An Extended Affinity Propagation Clustering Method Based on Different Data Density Types
title_full_unstemmed An Extended Affinity Propagation Clustering Method Based on Different Data Density Types
title_sort extended affinity propagation clustering method based on different data density types
description Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.
publisher Hindawi Publishing Corporation
publishDate 2015
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4317584/
_version_ 1613184340780384256

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