Fast Mining of Global Maximum Frequent Itemsets
نویسندگان
چکیده
منابع مشابه
Fast Parallel Mining of Frequent Itemsets
Association rule mining has become an essential data mining technique in various fields and the massive growth of the available data demands more and more computational power. To address this issue, it is necessary to study parallel implementations of such algorithms. In this paper, we propose a parallel approach to the Frequent Pattern Tree (FP-Tree) algorithm, which is a fast and popular tree...
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Node-list and N-list, two novel data structure proposed in recent years, have been proven to be very efficient for mining frequent itemsets. The main problem of these structures is that they both need to encode each node of a PPC-tree with pre-order and post-order code. This causes that they are memory consuming and inconvenient to mine frequent itemsets. In this paper, we propose Nodeset, a mo...
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Utility-based data mining is a new research area interested in all types of utility factors in data mining processes and targeted at incorporating utility considerations in both predictive and descriptive data mining tasks. High utility itemset mining is a research area of utilitybased descriptive data mining, aimed at finding itemsets that contribute most to the total utility. A specialized fo...
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Mining frequent itemsets from large databases is an important computational task with a lot of applications. The most known among them is the market-basket problem which assumes that we have a large number of items and we want to know which items are bought together. A recent application is that of web pages (baskets) and linked pages (items). Pages with many common references may be about the ...
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ژورنال
عنوان ژورنال: Journal of Software
سال: 2005
ISSN: 1000-9825
DOI: 10.1360/jos160553