Local Pattern Detection and Clustering: Are There Substantive Differences?
The starting point of this work is the definition of local pattern detection given in [10] as the unsupervised detection of local regions with anomalously high data density, which represent real underlying phenomena. We discuss some aspects of this definition and examine the differences between clus...
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description | The starting point of this work is the definition of local pattern detection given in [10] as the unsupervised detection of local regions with anomalously high data density, which represent real underlying phenomena. We discuss some aspects of this definition and examine the differences between clustering and pattern detection (if any), before we investigate how to utilize clustering algorithms for pattern detection. A modification of an existing clustering algorithm is proposed to identify local patterns that are flagged as being significant according to a statistical test. |
doi_str_mv | 10.1007/11504245_4 |
format | Conference Proceeding |
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We discuss some aspects of this definition and examine the differences between clustering and pattern detection (if any), before we investigate how to utilize clustering algorithms for pattern detection. A modification of an existing clustering algorithm is proposed to identify local patterns that are flagged as being significant according to a statistical test.</description><subject>Applied sciences</subject><subject>Association Rule Mining</subject><subject>Background Model</subject><subject>Computer science; control theory; systems</subject><subject>Data Density</subject><subject>Data Object</subject><subject>Exact sciences and technology</subject><subject>Information systems. Data bases</subject><subject>Local Pattern</subject><subject>Memory organisation. 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Data bases</topic><topic>Local Pattern</topic><topic>Memory organisation. Data processing</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Höppner, Frank</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Höppner, Frank</au><au>Siebes, Arno</au><au>Boulicaut, Jean-François</au><au>Morik, Katharina</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Local Pattern Detection and Clustering: Are There Substantive Differences?</atitle><btitle>Lecture notes in computer science</btitle><date>2005</date><risdate>2005</risdate><spage>53</spage><epage>70</epage><pages>53-70</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540265436</isbn><isbn>3540265430</isbn><eisbn>9783540318941</eisbn><eisbn>3540318941</eisbn><abstract>The starting point of this work is the definition of local pattern detection given in [10] as the unsupervised detection of local regions with anomalously high data density, which represent real underlying phenomena. We discuss some aspects of this definition and examine the differences between clustering and pattern detection (if any), before we investigate how to utilize clustering algorithms for pattern detection. A modification of an existing clustering algorithm is proposed to identify local patterns that are flagged as being significant according to a statistical test.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11504245_4</doi><tpages>18</tpages></addata></record> |
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identifier | ISSN: 0302-9743 |
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language | eng |
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source | Springer Books |
subjects | Applied sciences Association Rule Mining Background Model Computer science control theory systems Data Density Data Object Exact sciences and technology Information systems. Data bases Local Pattern Memory organisation. Data processing Software |
title | Local Pattern Detection and Clustering: Are There Substantive Differences? |
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