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» Statistical Supports for Frequent Itemsets on Data Streams
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IS
2007
14 years 11 months ago
Discovering frequent geometric subgraphs
As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the req...
Michihiro Kuramochi, George Karypis
PAKDD
2005
ACM
124views Data Mining» more  PAKDD 2005»
15 years 5 months ago
Finding Sporadic Rules Using Apriori-Inverse
We define sporadic rules as those with low support but high confidence: for example, a rare association of two symptoms indicating a rare disease. To find such rules using the w...
Yun Sing Koh, Nathan Rountree
PAKDD
2010
ACM
171views Data Mining» more  PAKDD 2010»
14 years 10 months ago
Summarizing Multidimensional Data Streams: A Hierarchy-Graph-Based Approach
With the rapid development of information technology, many applications have to deal with potentially infinite data streams. In such a dynamic context, storing the whole data stre...
Yoann Pitarch, Anne Laurent, Pascal Poncelet
JIIS
2006
119views more  JIIS 2006»
14 years 11 months ago
Answering constraint-based mining queries on itemsets using previous materialized results
Abstract In recent years, researchers have begun to study inductive databases, a new generation of databases for leveraging decision support applications. In this context, the user...
Roberto Esposito, Rosa Meo, Marco Botta
FIMI
2004
123views Data Mining» more  FIMI 2004»
15 years 1 months ago
Surprising Results of Trie-based FIM Algorithms
Trie is a popular data structure in frequent itemset mining (FIM) algorithms. It is memory-efficient, and allows fast construction and information retrieval. Many trie-related tec...
Ferenc Bodon