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On a visual frequent itemset mining
2009 Fourth International Conference on Digital Information Management, 2009Given a large, dense transaction database, generating interesting frequent patterns in a user friendly manner remains as an important issue in data mining. It is because the minimum support, the most popular statistical significance measurement, is not capable of reflecting the domain user's interest. This paper presents visual frequent itemset mining (
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Mining Frequent and Homogeneous Closed Itemsets
2016It is well known that when mining frequent itemsets from a transaction database, the output is usually too large to be effectively exploited by users. To cope with this difficulty, several forms of condensed representations of the set of frequent itemsets have been proposed, among which the notion of closure is one of the most popular.
Inès Hilali +4 more
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Efficient mining frequent itemsets algorithms
International Journal of Machine Learning and Cybernetics, 2013Efficient algorithms for mining frequent itemsets are crucial for mining association rules as well as for many other data mining tasks. It is well known that countTable is one of the most important facility to employ subsets property for compressing the transaction database to new lower representation of occurrences items. One of the biggest problem in
Marghny H. Mohamed +1 more
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An Improved Algorithm for Frequent Itemsets Mining
2017 Fifth International Conference on Advanced Cloud and Big Data (CBD), 2017Based on the classical FP-growth algorithm about frequent itemsets mining, this paper proposes a more efficient non-recursive FPNR-growth algorithm and corresponding data structure. The experimental results show that the FPNR-growth algorithm is superior to the FP-growth algorithm, both in mining time and in storage space.
Hao Jiang, Xu He
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Incremental Frequent Itemsets Mining with MapReduce
2017Frequent itemsets mining is a common task in data mining. Since sizes of today’s databases go far beyond capabilities of a single machine, recent studies show how to adopt classical algorithms for frequent itemsets mining for parallel frameworks such as MapReduce. Even then, in case of a slight database update a re-run of the MapReduce mining algorithm
Kirill Kandalov, Ehud Gudes
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A Decomposition Approach for Mining Frequent Itemsets
Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007), 2007In this paper, instead of proposing the fastest mining algorithm in the world, we present a new approach in mining association rules. We propose a new algorithm - GRA (Gradational Reduction Approach). It adopts three mechanisms to increase the performance of mining.
Jen-Peng Huang +3 more
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An algorithm for mining frequent closed itemsets
2008 3rd International Conference on Intelligent System and Knowledge Engineering, 2008The problem of mining frequent itemsets plays an essential role in mining association rules, but it is not necessary to mine all frequent itemsets, instead it is sufficient to mine the set of frequent closed itemsets, which is much smaller than the set of all frequent itemsets. In this paper, we present an efficient algorithm, FCI-Miner, for mining all
Tiejun Zhang, Junrui Yang, Xiuqin Wang
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Memory Efficient Frequent Itemset Mining
2018Frequent itemset mining has been one of the most popular data mining techniques. Despite a large number of algorithms developed to implement this functionality, there is still room for improvement of their efficiency. In this paper, we focus on memory use in frequent itemset mining.
Nima Shahbazi +2 more
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Mining Frequent Itemsets with Dualistic Constraints
2012Mining frequent itemsets can often generate a large number of frequent itemsets. Recent studies proposed mining itemset with the different types of constraint. The paper is to mine frequent itemsets, where a one: does not contain any item of C0 or contains at least one item of C0.
Anh N. Tran +3 more
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Distributed Frequent Closed Itemsets Mining
2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, 2007As many large organizations have multiple data sources and the scale of dataset becomes larger and larger, it is inevitable to carry out data mining in the distributed environment. In this paper, we address the problem of mining global frequent closed itemsets in distributed environment.
Chun Liu +3 more
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