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

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.

► A new framework, re-mining is proposed to use association mining (AM) in retailing.
► After finding product relations by using AM, re-mining forms an augmented dataset.
► A classification method then can be used to analyze the augmented dataset.
► Complexity analyses of re-mining and quantitative AM are done for comparison.
► Application of re-mining on a real dataset from retailing domain is demonstrated.