Efficiently mining association rules based on maximum single constraints
Vietnam Journal of Computer Science, 2017 A serious problem encountered during the mining of association rules is the exponential growth of their cardinality. Unfortunately, the known algorithms for mining association rules typically generate scores of redundant and duplicate rules. Thus, we not
Anh Tran, Tin Truong, Bac Le
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An efficient colossal closed itemset mining algorithm for a dataset with high dimensionality
Journal of King Saud University: Computer and Information Sciences, 2022 The greater interest of research in the field of bioinformatics and the ample amount of available data across the different domains paved the way for the generation of the dataset with high dimensionality.
Manjunath K. Vanahalli, Nagamma Patil
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Discovering Frequent Closed Itemsets for Association Rules [PDF]
, 1999 In this paper, we address the problem of finding frequent itemsets in a database. Using the closed itemset lattice framework, we show that this problem can be reduced to the problem of finding frequent closed itemsets. Based on this statement, we can construct efficient data mining algorithms by limiting the search space to the closed itemset lattice ...
Pasquier, Nicolas +3 more
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An efficient parallel method for mining frequent closed sequential patterns [PDF]
, 2017 Mining frequent closed sequential pattern (FCSPs) has attracted a great deal of research attention, because it is an important task in sequences mining.
Huynh, Bao, Snášel, Václav, Vo, Bay
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Mining frequent biological sequences based on bitmap without candidate sequence generation [PDF]
, 2015 Biological sequences carry a lot of important genetic information of organisms. Furthermore, there is an inheritance law related to protein function and structure which is useful for applications such as disease prediction.
Davis, Darryl N. +2 more
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Modified GUIDE (LM) algorithm for mining maximal high utility patterns from data streams [PDF]
International Journal of Computational Intelligence Systems, 2015 High utility pattern mining is an emerging research topic in the data mining field. Unlike frequent pattern mining, high utility pattern mining deals with non-binary databases, in which the information about purchased quantities of items is maintained ...
Chiranjeevi Manike, Hari Om
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Closed Frequent Itemset Mining with Arbitrary Side Constraints [PDF]
2018 IEEE International Conference on Data Mining Workshops (ICDMW), 2018 Frequent itemset mining (FIM) is a method for finding regularities in transaction databases. It has several application areas, such as market basket analysis, genome analysis, and drug design. Finding frequent itemsets allows further analysis to focus on a small subset of the data.
Kocak, Gokberk +3 more
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Incremental Closed Frequent Itemsets Mining-Based Approach Using Maximal Candidates
IEEE AccessIncremental frequent itemset mining aims to efficiently update frequent itemsets without recalculating them from scratch, making it suitable for streaming data and real-time analytics.
Mohammed A. Al-Zeiadi +1 more
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ETP-Mine: An Efficient Method for Mining Transitional Patterns [PDF]
, 2010 A Transaction database contains a set of transactions along with items and their associated timestamps. Transitional patterns are the patterns which specify the dynamic behavior of frequent patterns in a transaction database.
Bhaskar, A., Kumar, B. Kiran
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New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
Algorithms, 2018 The Map-Reduce (MR) framework has become a popular framework for developing new parallel algorithms for Big Data. Efficient algorithms for data mining of big data and distributed databases has become an important problem.
Yaron Gonen, Ehud Gudes, Kirill Kandalov
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