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Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Association Rules Mining for Retail Organizations

Association Rules Mining for Retail Organizations
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Author(s): Ioannis N. Kouris (University of Patras, Greece), Christos Makris (University of Patras, Greece), Evangelos Theodoridis (University of Patras, Greece) and Athanasios Tsakalidis (University of Patras, Greece)
Copyright: 2009
Pages: 6
Source title: Encyclopedia of Information Science and Technology, Second Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-60566-026-4.ch045

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Abstract

In recent years, we have witnessed an explosive growth in the amount of data generated and stored from practically all possible fields (e.g., science, business, medicine, military just to name a few). However, the ability to store more and more data has not been followed by the same rate of growth in the processing power, and, therefore, much of the data accumulated remains today still unanalyzed. Data mining, which could be defined as the process concerned with applying computational techniques (i.e., algorithms implemented as computer programs) to actually find patterns in the data, tries to bridge this gap. Among others, data mining technologies include association rule discovery, classification, clustering, summarization, regression and sequential pattern discovery (Adrians & Zantige, 1996; Chen, Han, & Yu, 1996; Fayyad, Piatetsky-Shapiro, & Smyth, 1996). This problem has been motivated by applications known as market basket analysis which find items purchased by customers; that is, what kinds of products tend to be purchased together (Agrawal, Imielinski, & Swami, 1993).

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