A quantitative correlation coefficient mining method for business intelligence in small and medium enterprises of trading business

Chi Fai Cheung, F. L. Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

31 Citations (Scopus)


Business intelligence based on data mining has been one of the popular and indispensable tools for identifying business opportunity in sales and marketing of new products. The traditional data mining methods based on association rules may be inadequate in completely uncovering the hidden patterns of sales based on transaction records. This paper presents a qualitative correlation coefficient mining method which is capable of uncovering hidden patterns of sales and market. Hence, a prototype business intelligence system (BIS) named correlation coefficient sales data mining system (CCSDMS) has been developed and successfully trial implemented in a selected reference site. A series of experiments have been conducted to evaluate the performance of the proposed system. The results generated by the BIS are compared with a well known market available data mining system. The proposed quantitative correlation coefficient mining method is found to possess higher accuracy, better computational effectiveness and higher predictive power. With the new approach, associations for product relations and customer periodic demands are revealed and this can help to leverage organizational marketing capital to enhance quality and speed of promotions as well as awareness of product relations.
Original languageEnglish
Pages (from-to)6279-6291
Number of pages13
JournalExpert Systems with Applications
Issue number7
Publication statusPublished - 1 Jun 2012


  • Business intelligence
  • Correlation coefficient mining
  • Data mining
  • Knowledge management
  • Knowledge system technology
  • Product relation network

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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