dc.contributor.author | Demiriz, Ayhan | en_US |
dc.contributor.author | Ertek, Gürdal | en_US |
dc.contributor.author | Atan, Sabri Tankut | en_US |
dc.contributor.author | Kula, Ufuk | en_US |
dc.date.accessioned | 2019-07-30T17:20:17Z | |
dc.date.available | 2019-07-30T17:20:17Z | |
dc.date.issued | 2010 | |
dc.identifier.citation | Demiriz, A., Ertek, G., Atan, S. T. & Kula, U. (2010). Re-mining positive and negative association mining results. Paper presented at the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6171, 101-114. doi:10.1007/978-3-642-14400-4_8 | en_US |
dc.identifier.isbn | 9783642143991 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.issn | 1611-3349 | |
dc.identifier.uri | https://hdl.handle.net/11729/1664 | |
dc.identifier.uri | https://doi.org/10.1007/978-3-642-14400-4_8 | |
dc.description | This work is financially supported by the Turkish Scientific Research Council under Grant TUBITAK 107M257. | en_US |
dc.description.abstract | Positive and negative association mining are well-known and extensively studied data mining techniques to analyze market basket data. Efficient algorithms exist to find both types of association, separately or simultaneously. Association mining is performed by operating on the transaction data. Despite being an integral part of the transaction data, the p::icing and time information has not been incorporated into market basket analysis so far, and additional attributes have been handled using quantitative association mining. In this paper, a new approach is proposed to incorporate price, time and domain related attributes into data mining by re-mining the association mining results. The underlying factors behind positive and negative relationships, as indicated by the association rules, are characterized and described through the second data ruining stage re-mining. The applicability of the methodology is demonstrated by analyzing data coming from apparel retailing industry, where price markdown is an essential tool for promoting sales and generating increased revenue. | en_US |
dc.description.sponsorship | Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer-Verlag Berlin | en_US |
dc.relation.isversionof | 10.1007/978-3-642-14400-4_8 | |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Rules | en_US |
dc.subject | Framework | en_US |
dc.subject | Association mining | en_US |
dc.subject | Data mining techniques | en_US |
dc.subject | Efficient algorithm | en_US |
dc.subject | Integral part | en_US |
dc.subject | Market basket | en_US |
dc.subject | Market basket analysis | en_US |
dc.subject | New approaches | en_US |
dc.subject | Time information | en_US |
dc.subject | Transaction data | en_US |
dc.subject | Underlying factors | en_US |
dc.subject | Algorithms | en_US |
dc.subject | Associative processing | en_US |
dc.subject | Economics | en_US |
dc.subject | Industry | en_US |
dc.title | Re-mining positive and negative association mining results | en_US |
dc.type | conferenceObject | en_US |
dc.description.version | Publisher's Version | en_US |
dc.relation.journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en_US |
dc.contributor.department | Işık Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü | en_US |
dc.contributor.department | Işık University, Faculty of Engineering, Department of Industrial Engineering | en_US |
dc.contributor.authorID | 0000-0002-3241-4617 | |
dc.identifier.volume | 6171 | |
dc.identifier.startpage | 101 | |
dc.identifier.endpage | 114 | |
dc.peerreviewed | Yes | en_US |
dc.publicationstatus | Published | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.contributor.institutionauthor | Atan, Sabri Tankut | en_US |
dc.relation.index | WOS | en_US |
dc.relation.index | Scopus | en_US |
dc.relation.index | Conference Proceedings Citation Index – Science (CPCI-S) | en_US |
dc.description.quality | Q4 | |
dc.description.wosid | WOS:000286902300008 | |