Results 141 to 150 of about 2,974 (185)

Mining Closed Itemsets: A Review

International Journal of Advancements in Computing Technology, 2012
Closed itemset mining is a popular research in data mining. It was proposed to avoid a large number of redundant itemsets in frequent itemset mining. Various algorithms were proposed with efficient strategies to generate closed itemsets. This paper aims to study the existence algorithms used to mine closed itemsets.
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A-Close+: An Algorithm for Mining Frequent Closed Itemsets

2008 International Conference on Advanced Computer Theory and Engineering, 2008
Association Rule Mining (ARM) is the most essential technique for data mining that mines hidden associations between data in large databases. The most important function of ARM is to find frequent itemsets. Frequent closed itemsets (FCI) is an important condense representation method for frequent itemsets, and because of its importance in recent years,
Maryam Shekofteh, Amir Masoud Rahmani, Mashaallah Abbasi Dezfuli   +2 more
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CloseMiner: Discovering Frequent Closed Itemsets Using Frequent Closed Tidsets

Fifth IEEE International Conference on Data Mining (ICDM'05), 2006
Complete set of itemsets can be grouped into non-overlapping clusters identified by closed tidsets. Each cluster has only one closed itemset and is the superset of all itemsets with the same support. Number of closed itemsets is identical to the number of clusters.
N.G. Singh, S.R. Singh, A.K. Mahanta
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Spatial Contextualization for Closed Itemset Mining

2018 IEEE International Conference on Data Mining (ICDM), 2018
We present the Spatial Contextualization for Closed Itemset Mining (SCIM) algorithm, an approach that builds a space for the target database in such a way that relevant itemsets can be retrieved regarding the relative spatial location of their items.
Altobelli Mantuan, Leandro Fernandes
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Frequent closed itemset based algorithms

ACM SIGKDD Explorations Newsletter, 2006
As a side effect of the digitalization of unprecedented amount of data, traditional retrieval tools proved to be unable to extract hidden and valuable knowledge. Data Mining, with a clear promise to provide adequate tools and/or techniques to do so, is the discovery of hidden information that can be retrieved from datasets.
Ben Yahia, S., Hamrouni, T., Mephu Nguifo, Engelbert   +2 more
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An efficient approach for mining cross-level closed itemsets and minimal association rules using closed itemset lattices

Expert Systems with Applications, 2014
Multilevel knowledge in transactional databases plays a significant role in our real-life market basket analysis. Many researchers have mined the hierarchical association rules and thus proposed various approaches. However, some of the existing approaches produce many multilevel and cross-level association rules that fail to convey quality information.
Tahrima Hashem, Chowdhury Farhan Ahmed, Md. Samiullah, Sayma Akther, Byeong-Soo Jeong, Seokhee Jeon   +5 more
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Finding Closed Itemsets in Data Streams

2005
Closed itemset mining is a difficult problem especially when we consider the task in the context of a data stream. Compared to mining from a static transaction data set, the streaming case has far more information to track and far greater complexity to manage.
Hai Wang, Wenyuan Li, Zengzhi Li, Lin Fan   +3 more
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Closed-Itemset Incremental-Mining Problem

2005
Association rules, introduced by Agrawal, Imielinski and Swami (1993), provide useful means to discover associations in data. The problem of mining association rules in a database is defined as finding all the association rules that hold with more than a user-given minimum support threshold and a user-given minimum confidence threshold.
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Parallel SAT based closed frequent itemsets enumeration

2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), 2015
Frequent itemset mining (FIM) is a useful task for discovering frequent co-occurring items. Since its inception, a number of significant FIM algorithms have been developed to speed up mining performances. Unfortunately, for huge dataset, scalability remains an important issue.
Imen Ouled Dlala, Said Jabbour, Lakhdar Sais, Yakoub Salhi, Boutheina Ben Yaghlane   +4 more
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Mining Approximate Closed Frequent Itemsets over Stream

2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008
Frequent itemset mining is a very important problem in data mining. Closed frequent itemsets is the condensed representation of frequent itemsets thus spend less memory, so it is much suitable for stream mining. But on the other hand, when the minimum support is much lower, the size of closed frequent itemsets turns larger, which makes the performance ...
Haifeng Li, Zongjian Lu, Hong Chen
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