Re-mining item associations: Methodology and a case study in apparel retailing

Ayhan Demiriz; Gürdal Ertek; Tankut Atan; Ufuk Kula

doi:10.1016/j.dss.2011.08.004

Re-mining item associations: Methodology and a case study in apparel retailing

Ayhan Demiriz, Gürdal Ertek, Tankut Atan, Ufuk Kula

Research output: Contribution to journal › Article › peer-review

16 Citations (Scopus)

Abstract

Association mining is the conventional data mining technique for analyzing market basket data and it reveals the positive and negative associations between items. While being an integral part of transaction data, pricing and time information have not been integrated into market basket analysis in earlier studies. This paper proposes a new approach to mine price, time and domain related attributes through re-mining of association mining results. The underlying factors behind positive and negative relationships can be characterized and described through this second data mining stage. The applicability of the methodology is demonstrated through the analysis of data coming from a large apparel retail chain, and its algorithmic complexity is analyzed in comparison to the existing techniques.

Original language	English
Pages (from-to)	284-293
Number of pages	10
Journal	Decision Support Systems
Volume	52
Issue number	1
DOIs	https://doi.org/10.1016/j.dss.2011.08.004
Publication status	Published - Dec 2011
Externally published	Yes

Keywords

Apparel retailing
Association mining
Data mining
Inductive decision trees
Negative association
Retail data

ASJC Scopus subject areas

Management Information Systems
Information Systems
Developmental and Educational Psychology
Arts and Humanities (miscellaneous)
Information Systems and Management

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10.1016/j.dss.2011.08.004

Cite this

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abstract = "Association mining is the conventional data mining technique for analyzing market basket data and it reveals the positive and negative associations between items. While being an integral part of transaction data, pricing and time information have not been integrated into market basket analysis in earlier studies. This paper proposes a new approach to mine price, time and domain related attributes through re-mining of association mining results. The underlying factors behind positive and negative relationships can be characterized and described through this second data mining stage. The applicability of the methodology is demonstrated through the analysis of data coming from a large apparel retail chain, and its algorithmic complexity is analyzed in comparison to the existing techniques.",

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AU - Demiriz, Ayhan

AU - Ertek, Gürdal

AU - Atan, Tankut

AU - Kula, Ufuk

N1 - Funding Information: This work is financially supported by the Turkish Scientific Research Council under Grant TUBITAK 107M257 . The authors would also like to thank one anonymous reviewer for helpful comments.

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AB - Association mining is the conventional data mining technique for analyzing market basket data and it reveals the positive and negative associations between items. While being an integral part of transaction data, pricing and time information have not been integrated into market basket analysis in earlier studies. This paper proposes a new approach to mine price, time and domain related attributes through re-mining of association mining results. The underlying factors behind positive and negative relationships can be characterized and described through this second data mining stage. The applicability of the methodology is demonstrated through the analysis of data coming from a large apparel retail chain, and its algorithmic complexity is analyzed in comparison to the existing techniques.

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KW - Association mining

KW - Data mining

KW - Inductive decision trees

KW - Negative association

KW - Retail data

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Re-mining item associations: Methodology and a case study in apparel retailing

Abstract

Keywords

ASJC Scopus subject areas

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