Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data

Fuzzy clustering algorithms are helpful when there exists a dataset with subgroupings of points having indistinct boundaries and overlap between the clusters. Traditional methods have been extensively studied and used on real-world data, but require users to have some knowledge of the outcome a prio...

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Main Authors:	David J. Miller, Carl A. Nelson, Molly Boeka Cannon, Kenneth P. Cannon
Format:	Article
Language:	English
Published:	Hindawi Limited 2009-01-01
Series:	Applied Computational Intelligence and Soft Computing
Online Access:	http://dx.doi.org/10.1155/2009/876361

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spelling	doaj-7e998c51c1584f9383a31201d6d5c6142020-11-25T00:35:03ZengHindawi LimitedApplied Computational Intelligence and Soft Computing1687-97241687-97322009-01-01200910.1155/2009/876361876361Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics DataDavid J. Miller0Carl A. Nelson1Molly Boeka Cannon2Kenneth P. Cannon3Department of Mechanical Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588-0656, USADepartment of Mechanical Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588-0656, USAGeography and Geographic Information Science, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583-0961, USAUSU Archaeological Services and Department of Sociology, Social Work, and Anthropology, Utah State University, Logan, UT 84322-0730, USAFuzzy clustering algorithms are helpful when there exists a dataset with subgroupings of points having indistinct boundaries and overlap between the clusters. Traditional methods have been extensively studied and used on real-world data, but require users to have some knowledge of the outcome a priori in order to determine how many clusters to look for. Additionally, iterative algorithms choose the optimal number of clusters based on one of several performance measures. In this study, the authors compare the performance of three algorithms (fuzzy c-means, Gustafson-Kessel, and an iterative version of Gustafson-Kessel) when clustering a traditional data set as well as real-world geophysics data that were collected from an archaeological site in Wyoming. Areas of interest in the were identified using a crisp cutoff value as well as a fuzzy α-cut to determine which provided better elimination of noise and non-relevant points. Results indicate that the α-cut method eliminates more noise than the crisp cutoff values and that the iterative version of the fuzzy clustering algorithm is able to select an optimum number of subclusters within a point set (in both the traditional and real-world data), leading to proper indication of regions of interest for further expert analysishttp://dx.doi.org/10.1155/2009/876361
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	David J. Miller Carl A. Nelson Molly Boeka Cannon Kenneth P. Cannon
spellingShingle	David J. Miller Carl A. Nelson Molly Boeka Cannon Kenneth P. Cannon Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data Applied Computational Intelligence and Soft Computing
author_facet	David J. Miller Carl A. Nelson Molly Boeka Cannon Kenneth P. Cannon
author_sort	David J. Miller
title	Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data
title_short	Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data
title_full	Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data
title_fullStr	Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data
title_full_unstemmed	Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data
title_sort	comparison of fuzzy clustering methods and their applications to geophysics data
publisher	Hindawi Limited
series	Applied Computational Intelligence and Soft Computing
issn	1687-9724 1687-9732
publishDate	2009-01-01
description	Fuzzy clustering algorithms are helpful when there exists a dataset with subgroupings of points having indistinct boundaries and overlap between the clusters. Traditional methods have been extensively studied and used on real-world data, but require users to have some knowledge of the outcome a priori in order to determine how many clusters to look for. Additionally, iterative algorithms choose the optimal number of clusters based on one of several performance measures. In this study, the authors compare the performance of three algorithms (fuzzy c-means, Gustafson-Kessel, and an iterative version of Gustafson-Kessel) when clustering a traditional data set as well as real-world geophysics data that were collected from an archaeological site in Wyoming. Areas of interest in the were identified using a crisp cutoff value as well as a fuzzy α-cut to determine which provided better elimination of noise and non-relevant points. Results indicate that the α-cut method eliminates more noise than the crisp cutoff values and that the iterative version of the fuzzy clustering algorithm is able to select an optimum number of subclusters within a point set (in both the traditional and real-world data), leading to proper indication of regions of interest for further expert analysis
url	http://dx.doi.org/10.1155/2009/876361
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Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data

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