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» Approximate data mining in very large relational data
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DATAMINE
2006
166views more  DATAMINE 2006»
15 years 1 months ago
Accelerated EM-based clustering of large data sets
Motivated by the poor performance (linear complexity) of the EM algorithm in clustering large data sets, and inspired by the successful accelerated versions of related algorithms l...
Jakob J. Verbeek, Jan Nunnink, Nikos A. Vlassis
RCIS
2010
15 years 4 hour ago
A Tree-based Approach for Efficiently Mining Approximate Frequent Itemsets
—The strategies for mining frequent itemsets, which is the essential part of discovering association rules, have been widely studied over the last decade. In real-world datasets,...
Jia-Ling Koh, Yi-Lang Tu
VLDB
2000
ACM
155views Database» more  VLDB 2000»
15 years 5 months ago
ICICLES: Self-Tuning Samples for Approximate Query Answering
Approximate query answering systems provide very fast alternatives to OLAP systems when applications are tolerant to small errors in query answers. Current sampling-based approach...
Venkatesh Ganti, Mong-Li Lee, Raghu Ramakrishnan
ICDE
2008
IEEE
124views Database» more  ICDE 2008»
16 years 3 months ago
Mining Approximate Order Preserving Clusters in the Presence of Noise
Subspace clustering has attracted great attention due to its capability of finding salient patterns in high dimensional data. Order preserving subspace clusters have been proven to...
Mengsheng Zhang, Wei Wang 0010, Jinze Liu
KDD
2001
ACM
203views Data Mining» more  KDD 2001»
16 years 2 months ago
Ensemble-index: a new approach to indexing large databases
The problem of similarity search (query-by-content) has attracted much research interest. It is a difficult problem because of the inherently high dimensionality of the data. The ...
Eamonn J. Keogh, Selina Chu, Michael J. Pazzani