An online algorithm for segmenting time series
In recent years, there has been an explosion of interest in mining time-series databases. As with most computer science problems, representation of the data is the key to efficient and effective solutions. One of the most commonly used representations is piecewise linear approximation. This represen...
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| creator | Keogh, E. Chu, S. Hart, D. Pazzani, M. |
| description | In recent years, there has been an explosion of interest in mining time-series databases. As with most computer science problems, representation of the data is the key to efficient and effective solutions. One of the most commonly used representations is piecewise linear approximation. This representation has been used by various researchers to support clustering, classification, indexing and association rule mining of time-series data. A variety of algorithms have been proposed to obtain this representation, with several algorithms having been independently rediscovered several times. In this paper, we undertake the first extensive review and empirical comparison of all proposed techniques. We show that all these algorithms have fatal flaws from a data-mining perspective. We introduce a novel algorithm that we empirically show to be superior to all others in the literature. |
| doi_str_mv | 10.1109/ICDM.2001.989531 |
| format | Conference Proceeding |
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We introduce a novel algorithm that we empirically show to be superior to all others in the literature.</description><subject>Association rules</subject><subject>Change detection algorithms</subject><subject>Clustering algorithms</subject><subject>Computer science</subject><subject>Data mining</subject><subject>Explosions</subject><subject>Indexing</subject><subject>Piecewise linear approximation</subject><subject>Piecewise linear techniques</subject><isbn>9780769511191</isbn><isbn>0769511198</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2001</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj81KxDAYRQMiKGP34iov0JovP02yHOrfwAxudD00zZcaaVNJuvHtLYyXC4e7OXAJuQfWADD7eOieTg1nDBprrBJwRSqrDdOtVQBg4YZUpXyzLVIBa_UtafaJLmmKCWk_jUuO69dMw5JpwXHGtMY00jXOuO0csdyR69BPBat_7sjny_NH91Yf318P3f5YD6BgrZ2XQSBwZzgzAwfNpZPOBWeC80IxbUFu5YO13FvsvTeh1Zo7DHpA8GJHHi7eiIjnnxznPv-eL6fEH06vQa8</recordid><startdate>2001</startdate><enddate>2001</enddate><creator>Keogh, E.</creator><creator>Chu, S.</creator><creator>Hart, D.</creator><creator>Pazzani, M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2001</creationdate><title>An online algorithm for segmenting time series</title><author>Keogh, E. ; Chu, S. ; Hart, D. ; Pazzani, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c151t-bd4f3e12b8208c21724b4bbfb8fbd35079149142c992d9eadd8f6772bef7ce1d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Association rules</topic><topic>Change detection algorithms</topic><topic>Clustering algorithms</topic><topic>Computer science</topic><topic>Data mining</topic><topic>Explosions</topic><topic>Indexing</topic><topic>Piecewise linear approximation</topic><topic>Piecewise linear techniques</topic><toplevel>online_resources</toplevel><creatorcontrib>Keogh, E.</creatorcontrib><creatorcontrib>Chu, S.</creatorcontrib><creatorcontrib>Hart, D.</creatorcontrib><creatorcontrib>Pazzani, M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Keogh, E.</au><au>Chu, S.</au><au>Hart, D.</au><au>Pazzani, M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An online algorithm for segmenting time series</atitle><btitle>Proceedings 2001 IEEE International Conference on Data Mining</btitle><stitle>ICDM</stitle><date>2001</date><risdate>2001</risdate><spage>289</spage><epage>296</epage><pages>289-296</pages><isbn>9780769511191</isbn><isbn>0769511198</isbn><abstract>In recent years, there has been an explosion of interest in mining time-series databases. 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| ispartof | Proceedings 2001 IEEE International Conference on Data Mining, 2001, p.289-296 |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Association rules Change detection algorithms Clustering algorithms Computer science Data mining Explosions Indexing Piecewise linear approximation Piecewise linear techniques |
| title | An online algorithm for segmenting time series |
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