Vol. 10 Issue 4
Year:2016
Issue:Apr-Jun
Title:Mining Fuzzy Association Rules Using Various Algorithms: A Survey
Author Name:O. Gireesha and O. Obulesu
Synopsis:
The discovery of Association Rules (AR) acquire an imperative rule in Data Mining, which tries to find correlation among the attributes in a database. Classical Association Rules are meant for Boolean data and they suffer from a sharp boundary problem in handling quantitative data. Thereby, Fuzzy Association Rules (FAR) with fuzzy minimum support and confidence is introduced. In Fuzzy Association Rule Mining (FARM), determining fuzzy sets, tuning membership functions and automatic design of fuzzy sets are prominent objectives. Hence, FARM can be viewed as a multi-objective optimization problem. In this paper, different algorithms for FARM are discussed with merits and demerits.
Year:2016
Issue:Apr-Jun
Title:Mining Fuzzy Association Rules Using Various Algorithms: A Survey
Author Name:O. Gireesha and O. Obulesu
Synopsis:
The discovery of Association Rules (AR) acquire an imperative rule in Data Mining, which tries to find correlation among the attributes in a database. Classical Association Rules are meant for Boolean data and they suffer from a sharp boundary problem in handling quantitative data. Thereby, Fuzzy Association Rules (FAR) with fuzzy minimum support and confidence is introduced. In Fuzzy Association Rule Mining (FARM), determining fuzzy sets, tuning membership functions and automatic design of fuzzy sets are prominent objectives. Hence, FARM can be viewed as a multi-objective optimization problem. In this paper, different algorithms for FARM are discussed with merits and demerits.
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