Sky-signatures: detecting and characterizing recurrent behavior in sequential data
This paper proposes the sky-signature model, an extension of the signature model Gautrais et al. (in: Proceedings of the Pacific-Asia conference on knowledge discovery and data mining (PAKDD), Springer, 2017b) to multi-objective optimization. The signature approach considers a sequence of itemsets,...
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| Veröffentlicht in: | Data mining and knowledge discovery 2024-03, Vol.38 (2), p.372-419 |
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| container_title | Data mining and knowledge discovery |
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| creator | Gautrais, Clément Cellier, Peggy Guyet, Thomas Quiniou, René Termier, Alexandre |
| description | This paper proposes the
sky-signature
model, an extension of the
signature
model Gautrais et al. (in: Proceedings of the Pacific-Asia conference on knowledge discovery and data mining (PAKDD), Springer, 2017b) to multi-objective optimization. The signature approach considers a sequence of itemsets, and given a number
k
it returns a segmentation of the sequence in
k
segments such that the number of items occuring in all segments is maximized. The limitation of this approach is that it requires to manually set
k
, and thus fixes the temporal granularity at which the data is analyzed. The sky-signature model proposed in this paper removes this requirement, and allows to examine the results at multiple levels of granularity, while keeping a compact output. This paper also proposes efficient algorithms to mine sky-signatures, as well as an experimental validation both real data both from the retail domain and from natural language processing (political speeches). |
| doi_str_mv | 10.1007/s10618-023-00949-1 |
| format | Article |
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sky-signature
model, an extension of the
signature
model Gautrais et al. (in: Proceedings of the Pacific-Asia conference on knowledge discovery and data mining (PAKDD), Springer, 2017b) to multi-objective optimization. The signature approach considers a sequence of itemsets, and given a number
k
it returns a segmentation of the sequence in
k
segments such that the number of items occuring in all segments is maximized. The limitation of this approach is that it requires to manually set
k
, and thus fixes the temporal granularity at which the data is analyzed. The sky-signature model proposed in this paper removes this requirement, and allows to examine the results at multiple levels of granularity, while keeping a compact output. This paper also proposes efficient algorithms to mine sky-signatures, as well as an experimental validation both real data both from the retail domain and from natural language processing (political speeches).</description><identifier>ISSN: 1384-5810</identifier><identifier>EISSN: 1573-756X</identifier><identifier>DOI: 10.1007/s10618-023-00949-1</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Artificial Intelligence ; Behavior ; Chemistry and Earth Sciences ; Computer Science ; Customers ; Data mining ; Data Mining and Knowledge Discovery ; Hard rock music ; Information Storage and Retrieval ; Knowledge discovery ; Marketing ; Multiple objective analysis ; Natural language processing ; Physics ; Segments ; Signatures ; Speeches ; Statistics for Engineering</subject><ispartof>Data mining and knowledge discovery, 2024-03, Vol.38 (2), p.372-419</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c304t-e535a5e375867c5d45a9872c5072b2891d0f374ed2f2c7a75dc7701d3ac3d7493</cites><orcidid>0000-0001-8486-9616 ; 0000-0002-4909-5843 ; 0000-0002-1495-2534</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10618-023-00949-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10618-023-00949-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,777,781,882,27905,27906,41469,42538,51300</link.rule.ids><backlink>$$Uhttps://hal.science/hal-04401641$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Gautrais, Clément</creatorcontrib><creatorcontrib>Cellier, Peggy</creatorcontrib><creatorcontrib>Guyet, Thomas</creatorcontrib><creatorcontrib>Quiniou, René</creatorcontrib><creatorcontrib>Termier, Alexandre</creatorcontrib><title>Sky-signatures: detecting and characterizing recurrent behavior in sequential data</title><title>Data mining and knowledge discovery</title><addtitle>Data Min Knowl Disc</addtitle><description>This paper proposes the
sky-signature
model, an extension of the
signature
model Gautrais et al. (in: Proceedings of the Pacific-Asia conference on knowledge discovery and data mining (PAKDD), Springer, 2017b) to multi-objective optimization. The signature approach considers a sequence of itemsets, and given a number
k
it returns a segmentation of the sequence in
k
segments such that the number of items occuring in all segments is maximized. The limitation of this approach is that it requires to manually set
k
, and thus fixes the temporal granularity at which the data is analyzed. The sky-signature model proposed in this paper removes this requirement, and allows to examine the results at multiple levels of granularity, while keeping a compact output. 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sky-signature
model, an extension of the
signature
model Gautrais et al. (in: Proceedings of the Pacific-Asia conference on knowledge discovery and data mining (PAKDD), Springer, 2017b) to multi-objective optimization. The signature approach considers a sequence of itemsets, and given a number
k
it returns a segmentation of the sequence in
k
segments such that the number of items occuring in all segments is maximized. The limitation of this approach is that it requires to manually set
k
, and thus fixes the temporal granularity at which the data is analyzed. The sky-signature model proposed in this paper removes this requirement, and allows to examine the results at multiple levels of granularity, while keeping a compact output. This paper also proposes efficient algorithms to mine sky-signatures, as well as an experimental validation both real data both from the retail domain and from natural language processing (political speeches).</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10618-023-00949-1</doi><tpages>48</tpages><orcidid>https://orcid.org/0000-0001-8486-9616</orcidid><orcidid>https://orcid.org/0000-0002-4909-5843</orcidid><orcidid>https://orcid.org/0000-0002-1495-2534</orcidid></addata></record> |
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| source | Springer Nature - Complete Springer Journals |
| subjects | Algorithms Artificial Intelligence Behavior Chemistry and Earth Sciences Computer Science Customers Data mining Data Mining and Knowledge Discovery Hard rock music Information Storage and Retrieval Knowledge discovery Marketing Multiple objective analysis Natural language processing Physics Segments Signatures Speeches Statistics for Engineering |
| title | Sky-signatures: detecting and characterizing recurrent behavior in sequential data |
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