Accelerating Fuzzy-C Means Using an Estimated Subsample Size

Many algorithms designed to accelerate the fuzzy c-means (FCM) clustering algorithm randomly sample the data. Typically, no statistical method is used to estimate the subsample size, despite the impact subsample sizes have on speed and quality. This paper introduces two new accelerated algorithms, i...

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Vydané v:	IEEE transactions on fuzzy systems Ročník 22; číslo 5; s. 1229 - 1244
Hlavní autori:	Parker, Jonathon K., Hall, Lawrence O.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.10.2014 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accelerated Acceleration Algorithm design and analysis Algorithms Clustering algorithms Estimates extensible fast fuzzy c-means (EFFCM) Fuzzy fuzzy c-means (FCM) fuzzy clustering Fuzzy logic Fuzzy set theory geometric progressive fuzzy c-means (GOFCM) minimum sample estimate random fuzzy c-means (MSERFCM) multinomial proportion online fuzzy c-means (OFCM) Optimization Partitioning algorithms Partitions progressive sampling random sampling plus extension fuzzy c-means (rseFCM) Runtime Samples sampling scalable single-pass fuzzy c-means (SPFCM) Statistical analysis Statistical methods stopping criterion rsefcm scalable progressive sampling sampling fuzzy c-means multinomial proportion accelerated effcm fcm mserfcm ofcm fuzzy clustering gofcm stopping criterion spfcm
ISSN:	1063-6706, 1941-0034
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Abstract	Many algorithms designed to accelerate the fuzzy c-means (FCM) clustering algorithm randomly sample the data. Typically, no statistical method is used to estimate the subsample size, despite the impact subsample sizes have on speed and quality. This paper introduces two new accelerated algorithms, i.e., geometric progressive fuzzy c-means (GOFCM) and minimum sample estimate random fuzzy c-means (MSERFCM), that use a statistical method to estimate the subsample size. GOFCM, which is a variant of single-pass fuzzy c-means (SPFCM), also leverages progressive sampling. MSERFCM, which is a variant of random sampling plus extension fuzzy c-means, gains a speedup from improved initialization. A general, novel stopping criterion for accelerated clustering is introduced. The new algorithms are compared with FCM and four accelerated variants of FCM. GOFCM's speedup was four-47 times that of FCM and faster than SPFCM on each of the six datasets that are used in the experiments. For five of the datasets, partitions were within 1% of those of FCM. MSERFCM's speedup was five-26 times that of FCM and produced partitions within 3% of those of FCM on all datasets. A unique dataset, consisting of plankton images, exposed the strengths and weaknesses of many of the algorithms tested. It is shown that the new stopping criterion is effective in speeding up algorithms such as SPFCM and the final partitions are very close to those of FCM.
AbstractList	Many algorithms designed to accelerate the Fuzzy c-Means (FCM) clustering algorithm randomly sample the data. Typically, no statistical method is used to estimate the subsample size, despite the impact subsample sizes have on speed and quality. This paper introduces two new accelerated algorithms, GOFCM and MSERFCM, that use a statistical method to estimate the subsample size. GOFCM, a variant of SPFCM, also leverages progressive sampling. MSERFCM, a variant of rseFCM, gains a speedup from improved initialization. A general, novel stopping criterion for accelerated clustering is introduced. The new algorithms are compared to FCM and four accelerated variants of FCM. GOFCM's speedup was 4-47 times that of FCM and faster than SPFCM on each of the six datasets used in experiments. For five of the datasets, partitions were within 1% of those of FCM. MSERFCM's speedup was 5-26 times that of FCM and produced partitions within 3% of those of FCM on all datasets. A unique dataset, consisting of plankton images, exposed the strengths and weaknesses of many of the algorithms tested. It is shown that the new stopping criterion is effective in speeding up algorithms such as SPFCM and the final partitions are very close to those of FCM. Many algorithms designed to accelerate the Fuzzy c-Means (FCM) clustering algorithm randomly sample the data. Typically, no statistical method is used to estimate the subsample size, despite the impact subsample sizes have on speed and quality. This paper introduces two new accelerated algorithms, GOFCM and MSERFCM, that use a statistical method to estimate the subsample size. GOFCM, a variant of SPFCM, also leverages progressive sampling. MSERFCM, a variant of rseFCM, gains a speedup from improved initialization. A general, novel stopping criterion for accelerated clustering is introduced. The new algorithms are compared to FCM and four accelerated variants of FCM. GOFCM's speedup was 4-47 times that of FCM and faster than SPFCM on each of the six datasets used in experiments. For five of the datasets, partitions were within 1% of those of FCM. MSERFCM's speedup was 5-26 times that of FCM and produced partitions within 3% of those of FCM on all datasets. A unique dataset, consisting of plankton images, exposed the strengths and weaknesses of many of the algorithms tested. It is shown that the new stopping criterion is effective in speeding up algorithms such as SPFCM and the final partitions are very close to those of FCM. Many algorithms designed to accelerate the fuzzy c-means (FCM) clustering algorithm randomly sample the data. Typically, no statistical method is used to estimate the subsample size, despite the impact subsample sizes have on speed and quality. This paper introduces two new accelerated algorithms, i.e., geometric progressive fuzzy c-means (GOFCM) and minimum sample estimate random fuzzy c-means (MSERFCM), that use a statistical method to estimate the subsample size. GOFCM, which is a variant of single-pass fuzzy c-means (SPFCM), also leverages progressive sampling. MSERFCM, which is a variant of random sampling plus extension fuzzy c-means, gains a speedup from improved initialization. A general, novel stopping criterion for accelerated clustering is introduced. The new algorithms are compared with FCM and four accelerated variants of FCM. GOFCM's speedup was four-47 times that of FCM and faster than SPFCM on each of the six datasets that are used in the experiments. For five of the datasets, partitions were within 1% of those of FCM. MSERFCM's speedup was five-26 times that of FCM and produced partitions within 3% of those of FCM on all datasets. A unique dataset, consisting of plankton images, exposed the strengths and weaknesses of many of the algorithms tested. It is shown that the new stopping criterion is effective in speeding up algorithms such as SPFCM and the final partitions are very close to those of FCM.
Author	Hall, Lawrence O. Parker, Jonathon K.
Author_xml	– sequence: 1 givenname: Jonathon K. surname: Parker fullname: Parker, Jonathon K. email: jkparker@mail.usf.edu organization: Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA – sequence: 2 givenname: Lawrence O. surname: Hall fullname: Hall, Lawrence O. email: hall@csee.usf.edu organization: Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA
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Cites_doi	10.1007/s11265-008-0243-1 10.1093/bioinformatics/btg119 10.1109/FUZZ-IEEE.2012.6250815 10.1109/NAFIPS.2008.4531233 10.1002/jmri.23801 10.1007/3-540-44795-4_17 10.1109/TIT.1982.1056489 10.1016/j.csda.2006.02.008 10.1007/BF01908075 10.1109/FUZZY.2004.1375677 10.1016/S0893-6080(97)00147-0 10.1109/42.996338 10.1109/TPAMI.2002.1023800 10.1016/j.compmedimag.2005.10.001 10.2307/2284239 10.1016/j.cviu.2013.05.001 10.1109/TFUZZ.2011.2179659 10.1016/j.patrec.2009.09.011 10.1002/nav.3800020109 10.1016/S0730-725X(97)00302-0 10.1109/TFUZZ.2003.809902 10.1109/TFUZZ.2011.2182354 10.1162/153244302760200678 10.1109/48.972110 10.1016/S0165-0114(96)00232-1 10.1109/IGARSS.1999.772030 10.1109/TSMCB.2002.1033179 10.1109/TFUZZ.2012.2201485 10.1109/ICDM.2001.989523 10.1002/int.20268 10.1109/91.995126 10.1109/TIE.2008.928111 10.1145/312129.312188 10.1007/978-1-4757-0450-1 10.1109/TFUZZ.2011.2175400 10.1109/TFUZZ.2011.2170175 10.1109/91.413225 10.1109/ICSMC.2010.5641870 10.1016/j.dsr.2003.09.008 10.2307/2684318 10.1109/TSMC.1987.6499296
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Snippet	Many algorithms designed to accelerate the fuzzy c-means (FCM) clustering algorithm randomly sample the data. Typically, no statistical method is used to... Many algorithms designed to accelerate the Fuzzy c-Means (FCM) clustering algorithm randomly sample the data. Typically, no statistical method is used to... Many algorithms designed to accelerate the Fuzzy c-Means (FCM) clustering algorithm randomly sample the data. Typically, no statistical method is used to...
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SubjectTerms	Accelerated Acceleration Algorithm design and analysis Algorithms Clustering algorithms Estimates extensible fast fuzzy c-means (EFFCM) Fuzzy fuzzy c-means (FCM) fuzzy clustering Fuzzy logic Fuzzy set theory geometric progressive fuzzy c-means (GOFCM) minimum sample estimate random fuzzy c-means (MSERFCM) multinomial proportion online fuzzy c-means (OFCM) Optimization Partitioning algorithms Partitions progressive sampling random sampling plus extension fuzzy c-means (rseFCM) Runtime Samples sampling scalable single-pass fuzzy c-means (SPFCM) Statistical analysis Statistical methods stopping criterion
Title	Accelerating Fuzzy-C Means Using an Estimated Subsample Size
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