Results 71 to 80 of about 1,488,653 (205)
Mining frequent closed itemsets out-of-core
, 2006 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 ...
Lucchese C, Perego R, Orlando S
core
Distributed mining of frequent closed itemsets: some preliminary results
, 2005 In this paper we address the problem of mining frequent closed itemsets in a distributed setting. We gure out an environment where a transactional dataset is horizontally partitioned and stored in di erent sites.
Lucchese C, Perego R, Orlando S
core
A False Negative Maximal Frequent Itemsets Mining Algorithm over Stream
, 2011 Maximal frequent itemsets are one of several condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space, thus being more suitable for stream mining.
Ning Zhang, Hai Feng Li
core +1 more source
Efficiently mining maximal frequent itemsets
Proceedings 2001 IEEE International Conference on Data Mining, 2002 We present GenMax, a backtracking search based algorithm for mining maximal frequent itemsets. GenMax uses a number of optimizations to prune the search space. It uses a novel technique called progressive focusing to perform maximality checking, and diffset propagation to perform fast frequency computation.
Karam Gouda, Mohammed Javeed Zaki
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Mining Frequent Closed Itemsets with the Frequent Pattern List
, 2008 The mining of the complete set of frequent itemsets will lead to a huge number of itemsets. Fortunately, this problem can be reduced to the mining of frequent closed itemsets (FCIs), which results in a much smaller number of itemsets.
Ching-chi Hsu +2 more
core
cdeNDI:a Efficient Algorithm for Mining Frequent Itemsets
, 2007 頻繁項目集的探勘，也就是從大型資料庫中找出頻繁項目集。這是許多其他問題的根本和基礎，像是關連規則、循序規則、分類和許多其他的課題。 在過去十年來，這個問題已經有了很大的進展。許多的演算法或改進現有演算法都不斷的被提出。然而，當我們降低最低支持度或是當我們遇到的資料庫是高度關連的時候，頻繁項目集的數目可能會極大。因此，如何應付密集資料庫仍然是一各具挑戰性的課題。 在這篇論文裡，我們提出cdeNDI這一種新演算法。這是以Eclat這個演算法為基礎，將closed itemsets和non ...
Huang, Chien-Ming, 黃健銘
core
Mining frequent itemsets from uncertain data
, 2007 We study the problem of mining frequent itemsets from uncertain data under a probabilistic framework. We consider transactions whose items are associated with existential probabilities and give a formal definition of frequent patterns under such an ...
Kao, B, Chui, CK, Hung, E
core
Frequent Itemset Mining in Big Data With Effective Single Scan Algorithms
IEEE Access, 2018 This paper considers frequent itemsets mining in transactional databases. It introduces a new accurate single scan approach for frequent itemset mining (SSFIM), a heuristic as an alternative approach (EA-SSFIM), as well as a parallel implementation on ...
Youcef Djenouri +3 more
doaj +1 more source
Frequent itemset mining on multiprocessor systems.
, 2014 Frequent itemset mining is an important building block in many data mining applications like market basket analysis, recommendation, web-mining, fraud detection, and gene expression analysis. In many of them, the datasets being mined can easily grow up to hundreds of gigabytes or even terabytes of data.
openaire +3 more sources
On Discovering Discriminative Itemsets Based on Detecting Frequent Itemsets in Succession
AlgorithmsDiscovery of Association Rules is one of the most common Data Mining techniques. Contrast data mining is a focused data mining research area for discovering interesting contrast patterns that state the significant differences between datasets, i.e ...
Aristotelis Kompothrekas +1 more
doaj +1 more source