Missing Value Imputation Method for Multiclass Matrix Data Based on Closed Itemset [PDF]
Entropy, 2022 Handling missing values in matrix data is an important step in data analysis. To date, many methods to estimate missing values based on data pattern similarity have been proposed. Most previously proposed methods perform missing value imputation based on
Mayu Tada +2 more
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Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules [PDF]
Sensors, 2020 In business, managers may use the association information among products to define promotion and competitive strategies. The mining of high-utility association rules (HARs) from high-utility itemsets enables users to select their own weights for rules ...
Thang Mai +4 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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Closed High Utility Pattern Mining over Data Stream Based on Projection in the Window
Taiyuan Ligong Daxue xuebao, 2022 A fast and effective algorithm EFIM_Closed_DS was proposed to mine closed and high utility itemsets in the data stream environment. The algorithm is based on the projection technology in the window, and the database projection technology and transaction ...
Muhang LI +4 more
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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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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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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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Closed Non-derivable Itemsets [PDF]
, 2006 Itemset mining typically results in large amounts of redundant itemsets. Several approaches such as closed itemsets, non-derivable itemsets and generators have been suggested for losslessly reducing the amount of itemsets. We propose a new pruning method based on combining techniques for closed and non-derivable itemsets that allows further reductions ...
Juho Muhonen, Hannu Toivonen
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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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