Density-based multiscale data condensation
A problem gaining interest in pattern recognition applied to data mining is that of selecting a small representative subset from a very large data set. In this article, a nonparametric data reduction scheme is suggested. It attempts to represent the density underlying the data. The algorithm selects...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2002-06, Vol.24 (6), p.734-747 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
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| creator | Mitra, P. Murthy, C.A. Pal, S.K. |
| description | A problem gaining interest in pattern recognition applied to data mining is that of selecting a small representative subset from a very large data set. In this article, a nonparametric data reduction scheme is suggested. It attempts to represent the density underlying the data. The algorithm selects representative points in a multiscale fashion which is novel from existing density-based approaches. The accuracy of representation by the condensed set is measured in terms of the error in density estimates of the original and reduced sets. Experimental studies on several real life data sets show that the multiscale approach is superior to several related condensation methods both in terms of condensation ratio and estimation error. The condensed set obtained was also experimentally shown to be effective for some important data mining tasks like classification, clustering, and rule generation on large data sets. Moreover, it is empirically found that the algorithm is efficient in terms of sample complexity. |
| doi_str_mv | 10.1109/TPAMI.2002.1008381 |
| format | Article |
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In this article, a nonparametric data reduction scheme is suggested. It attempts to represent the density underlying the data. The algorithm selects representative points in a multiscale fashion which is novel from existing density-based approaches. The accuracy of representation by the condensed set is measured in terms of the error in density estimates of the original and reduced sets. Experimental studies on several real life data sets show that the multiscale approach is superior to several related condensation methods both in terms of condensation ratio and estimation error. The condensed set obtained was also experimentally shown to be effective for some important data mining tasks like classification, clustering, and rule generation on large data sets. Moreover, it is empirically found that the algorithm is efficient in terms of sample complexity.</description><identifier>ISSN: 0162-8828</identifier><identifier>EISSN: 1939-3539</identifier><identifier>DOI: 10.1109/TPAMI.2002.1008381</identifier><identifier>CODEN: ITPIDJ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Clustering algorithms ; Condensing ; Data mining ; Data reduction ; Density ; Density measurement ; Error analysis ; Estimation error ; Intelligence ; Iterative algorithms ; Nearest neighbor searches ; Pattern recognition ; Sampling methods ; Studies ; Vector quantization</subject><ispartof>IEEE transactions on pattern analysis and machine intelligence, 2002-06, Vol.24 (6), p.734-747</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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In this article, a nonparametric data reduction scheme is suggested. It attempts to represent the density underlying the data. The algorithm selects representative points in a multiscale fashion which is novel from existing density-based approaches. The accuracy of representation by the condensed set is measured in terms of the error in density estimates of the original and reduced sets. Experimental studies on several real life data sets show that the multiscale approach is superior to several related condensation methods both in terms of condensation ratio and estimation error. The condensed set obtained was also experimentally shown to be effective for some important data mining tasks like classification, clustering, and rule generation on large data sets. Moreover, it is empirically found that the algorithm is efficient in terms of sample complexity.</description><subject>Algorithms</subject><subject>Clustering algorithms</subject><subject>Condensing</subject><subject>Data mining</subject><subject>Data reduction</subject><subject>Density</subject><subject>Density measurement</subject><subject>Error analysis</subject><subject>Estimation error</subject><subject>Intelligence</subject><subject>Iterative algorithms</subject><subject>Nearest neighbor searches</subject><subject>Pattern recognition</subject><subject>Sampling methods</subject><subject>Studies</subject><subject>Vector quantization</subject><issn>0162-8828</issn><issn>1939-3539</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2002</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqNkbtOwzAUhi0EEqXwArBUDCAhpRzfEnusyq1SEQxltlz7WEqVJiVOhr5906YDYkBMZzjffy76CLmmMKYU9OPic_I-GzMANqYAiit6QgZUc51wyfUpGQBNWaIUU-fkIsYVABUS-IA8PGEZ82abLG1EP1q3RZNHZwscedvYkatK3wG2yavykpwFW0S8OtYh-Xp5XkzfkvnH62w6mSeOq7RJtBRAPcpMOgVeaxdAWr_UAh2kdMkCk0I7pTBIzoKUkHEvgtDCU1Q6IB-S-37upq6-W4yNWXcnYVHYEqs2Gg2ZzlKasY68-5NkijEJ7B9gBipVSnTg7S9wVbV12b1rurbQGRzWsh5ydRVjjcFs6nxt662hYPY6zEGH2eswRx1d6KYP5Yj4I9B3dxcXhBw</recordid><startdate>20020601</startdate><enddate>20020601</enddate><creator>Mitra, P.</creator><creator>Murthy, C.A.</creator><creator>Pal, S.K.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>20020601</creationdate><title>Density-based multiscale data condensation</title><author>Mitra, P. ; Murthy, C.A. ; Pal, S.K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c386t-95401de575c80d99cf05adb94ec061b2f2549c88ef532f55073d4f494d1e89fe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Algorithms</topic><topic>Clustering algorithms</topic><topic>Condensing</topic><topic>Data mining</topic><topic>Data reduction</topic><topic>Density</topic><topic>Density measurement</topic><topic>Error analysis</topic><topic>Estimation error</topic><topic>Intelligence</topic><topic>Iterative algorithms</topic><topic>Nearest neighbor searches</topic><topic>Pattern recognition</topic><topic>Sampling methods</topic><topic>Studies</topic><topic>Vector quantization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mitra, P.</creatorcontrib><creatorcontrib>Murthy, C.A.</creatorcontrib><creatorcontrib>Pal, S.K.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mitra, P.</au><au>Murthy, C.A.</au><au>Pal, S.K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Density-based multiscale data condensation</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><date>2002-06-01</date><risdate>2002</risdate><volume>24</volume><issue>6</issue><spage>734</spage><epage>747</epage><pages>734-747</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><coden>ITPIDJ</coden><abstract>A problem gaining interest in pattern recognition applied to data mining is that of selecting a small representative subset from a very large data set. In this article, a nonparametric data reduction scheme is suggested. It attempts to represent the density underlying the data. The algorithm selects representative points in a multiscale fashion which is novel from existing density-based approaches. The accuracy of representation by the condensed set is measured in terms of the error in density estimates of the original and reduced sets. Experimental studies on several real life data sets show that the multiscale approach is superior to several related condensation methods both in terms of condensation ratio and estimation error. The condensed set obtained was also experimentally shown to be effective for some important data mining tasks like classification, clustering, and rule generation on large data sets. Moreover, it is empirically found that the algorithm is efficient in terms of sample complexity.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TPAMI.2002.1008381</doi><tpages>14</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Clustering algorithms Condensing Data mining Data reduction Density Density measurement Error analysis Estimation error Intelligence Iterative algorithms Nearest neighbor searches Pattern recognition Sampling methods Studies Vector quantization |
| title | Density-based multiscale data condensation |
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