ck-FARM: An R package to discover big data associations for business intelligence
SoftwareX, 2023 Fuzzy association rule mining (FARM) is a well-known data mining algorithm to identify frequently occurring patterns from datasets, in which the fuzzy set theory is applied to consider linguistic variables for building an explainable reasoning system. In
George To Sum Ho +3 more
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A trust-based recommender system for e-Learning environment using fuzzy clustering [PDF]
Fanāvarī-i āmūzish, 2021 Background and Objectives: Many conventional e-Learning systems are based on static information and consider all learners the same, so they cannot meet their diverse needs and tastes.
R. Mohamadrezaei, R. Ravanmehr
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Using nonstationary fuzzy sets to improve the tractability of fuzzy association rules [PDF]
, 2013 Modern organisations now collect very large volumes of data about customers, suppliers and other factors which may impact upon their business. There is a clear need to be able to mine this data and present it to decision makers in a clear and coherent ...
Matthews, Stephen G. +3 more
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Web Usage Mining with Evolutionary Extraction of Temporal Fuzzy Association Rules [PDF]
, 2013 In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the
Gongora, Mario A. +5 more
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Incremental Fuzzy Association Rule Mining for Classification and Regression
IEEE Access, 2019 The aim of mining fuzzy association rules is to find both the association and the casual relationships between the itemsets. With the arrival of dynamic data, the fuzzy association rules should be updated in real time.
Ling Wang, Qian Ma, Jianyao Meng
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Selection of Satisfied Association Rules via Aggregation of Linguistic Satisfied Degrees
IEEE Access, 2019 Many association rule mining algorithms have been well-established, such as Apriori, Eclat, FP-Growth, or LCM algorithms. However, the challenge is that the huge size of association rules is extracted by using these algorithms, and it is difficult for ...
Fangling Ren, Zheng Pei, Kehong Wu
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Combines the Apriori and FCM Algorithm to Improve the Extracted Association Rules with Determine the Minimum Support Automatically [PDF]
Journal of Information Technology Management, 2015 Apriori algorithm is the most popular algorithm in association rules mining. One of the problems the Apriori algorithm is that the user must specify a minimum support threshold.
Heydar Jafarzadeh +2 more
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Quality measures for fuzzy predicates in conjunctive and disjunctive normal forms
Ingeniería e Investigación, 2014 Association rule mining is a very popular data mining technique. Rules in this technique are often used to identify and represent de-pendencies between attributes in databases.
Taymi Ceruto Cordovés +2 more
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A Strategic Study of Mining Fuzzy Association Rules Using Fuzzy Multiple Correlation Measues
Journal of Algorithms & Computational Technology, 2012 Two different data variables may behave very similarly. Correlation is the problem of determining how much alike the two variables actually are and association rules are used just to show the relationships between data items.
Robinson P. John +2 more
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Mining Fuzzy Coherent Rules from Quantitative Transactions Without Minimum Support Threshold [PDF]
, 2012 Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, some comment problems of those approaches are that (1) a minimum support should
Chne, Chun-hao; Li, Ai-fang; Lee, Yeong-chyi; Hong, Tzung-pei +1 more
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