Results 1 to 10 of about 199 (141)
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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Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data [PDF]
The Scientific World Journal, 2014 During the last decade various algorithms have been developed and proposed for discovering overlapping clusters in high-dimensional data. The two most prominent application fields in this research, proposed independently, are frequent itemset mining ...
András Király +2 more
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High Scalability Document Clustering Algorithm Based On Top-K Weighted Closed Frequent Itemsets
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2021 Documents clustering based on frequent itemsets can be regarded a new method of documents clustering which is aimed to overcome curse of dimensionality of items produced by documents being clustered.
Gede Aditra Pradnyana, Arif Djunaidy
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Proposed Algorithm for Extracting Association Rule Depend on Closed Frequent Itemset (EACFI) [PDF]
Engineering and Technology Journal, 2011 Association rules are important one of data mining activities. All algorithms of association rule mining consist of finding frequency of itemsets, which satisfy a minimum support threshold, and then compute confidence percentage for each k-itemsets to ...
Emad k. Jbbar, Yaser Munther
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Concept Lattice Method for Spatial Association Discovery in the Urban Service Industry
ISPRS International Journal of Geo-Information, 2020 A relative lag in research methods, technical means and research paradigms has restricted the rapid development of geography and urban computing. Hence, there is a certain gap between urban data and industry applications.
Weihua Liao, Zhiheng Zhang, Weiguo Jiang
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Efficient Associate Rules Mining Based on Topology for Items of Transactional Data
Mathematics, 2023 A challenge in association rules’ mining is effectively reducing the time and space complexity in association rules mining with predefined minimum support and confidence thresholds from huge transaction databases.
Bo Li, Zheng Pei, Chao Zhang, Fei Hao
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Mining frequent closed itemsets with the frequent pattern list [PDF]
Proceedings 2001 IEEE International Conference on Data Mining, 2002 The mining of a complete set of frequent itemsets will lead to a huge number of itemsets. Fortunately, this problem can be reduced to the mining of frequent closed itemsets (FCIs), which results in a much smaller number of itemsets. The approaches to mining frequent closed itemsets can be categorized into two groups: those with candidate generation and
Tseng, Fan-Chen +2 more
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Mining frequent closed itemsets out of core [PDF]
Proceedings of the 2006 SIAM International Conference on Data Mining, 2006 Extracting frequent itemsets is an important task in many data mining applications. When data are very large, it becomes mandatory to perform the mining task by using an external memory algorithm, but only a few of these algorithms have been proposed so far.
LUCCHESE, Claudio +2 more
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An algebraic semigroup method for discovering maximal frequent itemsets
Open Mathematics, 2022 Discovering maximal frequent itemsets is an important issue and key technique in many data mining problems such as association rule mining. In the literature, generating maximal frequent itemsets proves either to be NP-hard or to have O(l34l(m+n))O\left({
Liu Jiang +5 more
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FCHUIM: Efficient Frequent and Closed High-Utility Itemsets Mining
IEEE Access, 2020 Mining a closed high-utility itemset is a prevalent research task in analyzing transaction databases. However, numerous target itemsets are generated in the closed high-utility itemset mining task.
Tianyou Wei +5 more
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