Mining Quantitative Frequent Itemsets Using Adaptive Density-based Subspace Clustering
نویسندگان
چکیده
منابع مشابه
Text clustering using frequent itemsets
Frequent itemset originates from association rule mining. Recently, it has been applied in text mining such as document categorization, clustering, etc. In this paper, we conduct a study on text clustering using frequent itemsets. The main contribution of this paper is three manifolds. First, we present a review on existing methods of document clustering using frequent patterns. Second, a new m...
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Frequent itemsets mining is a classic problem in data mining and plays an important role in data mining research for over a decade. However, the mining of the all frequent itemsets will lead to a massive number of itemsets. Fortunately, this problem can be reduced to the mining of maximal frequent itemsets. In this paper, we propose a new method for mining maximal frequent itemsets. Our method ...
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Interesting patterns often occur at varied levels of support. The classic association mining based on a uniform minimum support, such as Apriori, either misses interesting patterns of low support or suuers from the bottleneck of itemset generation. A better solution is to exploit support constraints, which specify what minimum support is required for what itemsets, so that only necessary itemse...
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We present a depth-first algorithm, PatriciaMine, that discovers all frequent itemsets in a dataset, for a given support threshold. The algorithm is main-memory based and employs a Patricia trie to represent the dataset, which is space efficient for both dense and sparse datasets, whereas alternative representations were adopted by previous algorithms for these two cases. A number of optimizati...
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ژورنال
عنوان ژورنال: Transactions of the Japanese Society for Artificial Intelligence
سال: 2006
ISSN: 1346-0714,1346-8030
DOI: 10.1527/tjsai.21.439