Improvements of IncSpan: Incremental Mining of Sequential Patterns in Large Database
In reality, sequence databases are updated incrementally. The changes on the database may invalidate some existing sequential patterns and introduce new ones. Instead of recomputing the database each time, the incremental mining algorithms target efficiently maintaining the sequential patterns in th...
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| creator | Nguyen, Son N. Sun, Xingzhi Orlowska, Maria E. |
| description | In reality, sequence databases are updated incrementally. The changes on the database may invalidate some existing sequential patterns and introduce new ones. Instead of recomputing the database each time, the incremental mining algorithms target efficiently maintaining the sequential patterns in the dynamically changing database.
Recently, a new incremental mining algorithm, called IncSpan was proposed at the International Conference on Knowledge Discovery and Data Mining (KDD’04). However, we find that in general, IncSpan fails to mine the complete set of sequential patterns from an updated database. In this paper, we clarify this weakness by proving the incorrectness of the basic properties in the IncSpan algorithm. Also, we rectify the observed shortcomings by giving our solution. |
| doi_str_mv | 10.1007/11430919_52 |
| format | Conference Proceeding |
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Recently, a new incremental mining algorithm, called IncSpan was proposed at the International Conference on Knowledge Discovery and Data Mining (KDD’04). However, we find that in general, IncSpan fails to mine the complete set of sequential patterns from an updated database. In this paper, we clarify this weakness by proving the incorrectness of the basic properties in the IncSpan algorithm. Also, we rectify the observed shortcomings by giving our solution.</description><subject>Algorithm</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Data processing. List processing. Character string processing</subject><subject>Exact sciences and technology</subject><subject>Incremental mining</subject><subject>Learning and adaptive systems</subject><subject>Memory organisation. Data processing</subject><subject>Sequential patterns</subject><subject>Software</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540260769</isbn><isbn>3540260765</isbn><isbn>3540319352</isbn><isbn>9783540319351</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkMtOwzAQRc1Loi1d8QPZsGARmPEjttmhlkelIpBa1pHjOFWgdYIdkPh7EsqCzczonKtZXELOEa4QQF4jcgYadS7oARkzwYGhZoIekhFmiCljXB-RqZZqcDQDmeljMgIGNNWSs1MyjvENAKjUdETWi10bmi-3c76LSVMlC29XrfE3wxF-sdkmT7Wv_WbQK_fx2bO6hy-m61zwMal9sjRh45K56UxhojsjJ5XZRjf92xPyen-3nj2my-eHxex2mbYUdZcKbbhVlbRZIQteSSyBcqZA8VKpApEpazPHSjYMa51VpUUpSnRWSC4cm5CL_d_WRGu2VTDe1jFvQ70z4TvHTGkBoPvc5T4Xe-U3LuRF07zHHCEfOs3_dcp-AGCUY_0</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Nguyen, Son N.</creator><creator>Sun, Xingzhi</creator><creator>Orlowska, Maria E.</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2005</creationdate><title>Improvements of IncSpan: Incremental Mining of Sequential Patterns in Large Database</title><author>Nguyen, Son N. ; Sun, Xingzhi ; Orlowska, Maria E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p219t-59a4c8f7c6b7b4f71d02438084d88b1138cc6e3d36e3dccec8dc175d1ec5745e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Algorithm</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Data processing. List processing. Character string processing</topic><topic>Exact sciences and technology</topic><topic>Incremental mining</topic><topic>Learning and adaptive systems</topic><topic>Memory organisation. Data processing</topic><topic>Sequential patterns</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nguyen, Son N.</creatorcontrib><creatorcontrib>Sun, Xingzhi</creatorcontrib><creatorcontrib>Orlowska, Maria E.</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nguyen, Son N.</au><au>Sun, Xingzhi</au><au>Orlowska, Maria E.</au><au>Cheung, David</au><au>Ho, Tu Bao</au><au>Liu, Huan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Improvements of IncSpan: Incremental Mining of Sequential Patterns in Large Database</atitle><btitle>Advances in Knowledge Discovery and Data Mining</btitle><date>2005</date><risdate>2005</risdate><spage>442</spage><epage>451</epage><pages>442-451</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540260769</isbn><isbn>3540260765</isbn><eisbn>3540319352</eisbn><eisbn>9783540319351</eisbn><abstract>In reality, sequence databases are updated incrementally. The changes on the database may invalidate some existing sequential patterns and introduce new ones. Instead of recomputing the database each time, the incremental mining algorithms target efficiently maintaining the sequential patterns in the dynamically changing database.
Recently, a new incremental mining algorithm, called IncSpan was proposed at the International Conference on Knowledge Discovery and Data Mining (KDD’04). However, we find that in general, IncSpan fails to mine the complete set of sequential patterns from an updated database. In this paper, we clarify this weakness by proving the incorrectness of the basic properties in the IncSpan algorithm. Also, we rectify the observed shortcomings by giving our solution.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11430919_52</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Advances in Knowledge Discovery and Data Mining, 2005, p.442-451 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_16895009 |
| source | Springer Books |
| subjects | Algorithm Applied sciences Artificial intelligence Computer science control theory systems Data processing. List processing. Character string processing Exact sciences and technology Incremental mining Learning and adaptive systems Memory organisation. Data processing Sequential patterns Software |
| title | Improvements of IncSpan: Incremental Mining of Sequential Patterns in Large Database |
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