Fast and memory efficient mining of frequent closed itemsets
نویسندگان
چکیده
منابع مشابه
Efficient Vertical Mining of Frequent Closed Itemsets and Generators
The effective construction of many association rule bases require the computation of both frequent closed and frequent generator itemsets (FCIs/FGs). However, these two tasks are rarely combined. Most of the existing solutions apply levelwise breadth-first traversal, though depth-first traversal is knowingly superior. Hence, we address here the depth-first FCI/FG-mining. The proposed algorithm,...
متن کاملDCI Closed: A Fast and Memory Efficient Algorithm to Mine Frequent Closed Itemsets
One of the main problems raising up in the frequent closed itemsetsmining problem is the duplicate detection. In this paper we propose a general technique for promptly detecting and discarding duplicate closed itemsets, without the need of keeping in the main memory the whole set of closed patterns. Our approach can be exploited with substantial performance benefits by any algorithm that adopts...
متن کاملEfficient Incremental Mining of Top-K Frequent Closed Itemsets
In this work we study the mining of top-K frequent closed itemsets, a recently proposed variant of the classical problem of mining frequent closed itemsets where the support threshold is chosen as the maximum value sufficient to guarantee that the itemsets returned in output be at least K. We discuss the effectiveness of parameter K in controlling the output size and develop an efficient algori...
متن کاملCLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets
Association mining may often derive an undesirably large set of frequent itemsets and association rules. Recent studies have proposed an interesting alternative: mining frequent closed itemsets and their corresponding rules, which has the same power as association mining but substantially reduces the number of rules to be presented. In this paper, we propose an e cient algorithm, CLOSET, for mi...
متن کاملMining frequent closed itemsets out-of-core
Extracting frequent itemsets is an important task in many data mining applications. When data are very large, it becomes mandatory to perform the mining task by using an external memory algorithm, but only a few of these algorithms have been proposed so far. Since also the result set of all the frequent itemsets is likely to be undesirably large, condensed representations, such as closed itemse...
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ژورنال
عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering
سال: 2006
ISSN: 1041-4347
DOI: 10.1109/tkde.2006.10