Results 21 to 30 of about 1,634,075 (173)
A novel biclustering approach to association rule mining for predicting HIV-1-human protein interactions. [PDF]
PLoS ONE, 2012 Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions.
Anirban Mukhopadhyay +2 more
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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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Class Association Rule Pada Metode Associative Classification
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2011 Frequent patterns (itemsets) discovery is an important problem in associative classification rule mining. Differents approaches have been proposed such as the Apriori-like, Frequent Pattern (FP)-growth, and Transaction Data Location (Tid)-list ...
Eka Karyawati, Edi Winarko
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Vertical Fragmentation for Database Using FPClose Algorithm
Journal of Information Security and Cybercrimes Research, 2019 Vertical fragmentation technique is used to enhance the performance of database system and reduce the number of access to irrelevant instances by splitting a table or relation into different fragments vertically.
Arwa S. Al-Shannaq, Sultan Almotairi
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PLoS ONE, 2015 Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples.
Ujjwal Maulik +3 more
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IEEE AccessTraditional pattern mining algorithms are based on tree and linked list structures. However, they often only consider a single factor of frequency or utility and have to deal with exponential search spaces as well as generate numerous candidates.
Xiumei Zhao, Xincheng Zhong, Bing Han
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Efficient Algorithms for Mining Erasable Closed Patterns From Product Datasets
IEEE Access, 2017 Finding knowledge from large data sets to use in intelligent systems becomes more and more important in the Internet era. Pattern mining, classification, text mining, and opinion mining are the topical issues.
Bay Vo +3 more
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INFORMATION TECHNOLOGY IN INDUSTRY, 2021 Frequent itemset (FI) mining is an interesting data mining task. Instead of directly mining the FIs from data it is preferred to mine only the closed frequent itemsets (CFIs) first and then extract the FIs for each CFI. However, some algorithms require the generators for each CFI in order to extract the FIs, leading to an extra cost.
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A Patricia-Tree Approach For Frequent Closed Itemsets
, 2007 {"references": ["J.S. Park, M.S. Chen and P.S. Yu, \"An Effective Hash Based Algorithm for Mining Association Rules,\" in Proc. 5th SIGMOD Intl. W orkshop. Management of Data, California, 1995, pp. 175-186.", "R. Agrawal and R. Srikant, \"Fast Algorithms for Mining Association\nRules. 20th Intl. Conf. Very Large Data Bases, Santiago, 1994, pp.
Moez Ben Hadj Hamida, Yahya Slimani
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An Algorithm of Mining Closed Frequent Itemsets [PDF]
Advances in Computer Science Research, 2016 Closed frequent itemset is a perfect representation of frequent itemset. This paper tries to find an efficient solution to mine the closed frequent itemsets over databases by sampling technique. We employ the SCFI tree to record the data synopsis of the frequent itemsets, and propose an efficient algorithm SCFI to maintain the SCFI.
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