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The flood of data has led to new techniques with the ability to assist humans intelligently and automatically in analyzing the overflow of data for retrieving useful knowledge. Association mining is an important problem in data mining. A lot of research contributing to association rules has been proposed in recent years. Many of them are the algorithms that effectively deal with a large itemset method. Although these algorithms increase the efficiency of association mining, they have some critical problems such as flexibility, substantial computational efforts, and redundant comparisons for generating rules. In this thesis, we propose an alternative approach for the problem of mining association rules based on Formal Concept Analysis. Using this approach, association rules can be discovered dynamically, and the cost of generating rules can be reduced. We also show that the many-valued context of Formal Concept Analysis could be used for finding more detailed quantitative information. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .J66. Source: Masters Abstracts International, Volume: 40-06, page: 1547. Adviser: Young Park. Thesis (M.Sc.)--University of Windsor (Canada), 2002.
Kim, Jong-Seok., "Mining association rules using formal concept analysis." (2002). Electronic Theses and Dissertations. 2739.