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DATAMINE
2006
166views more  DATAMINE 2006»
13 years 5 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
ICDAR
2003
IEEE
13 years 10 months ago
Accelerating Large Character Set Recognition using Pivots
This paper proposes a method to accelerate character recognition of a large character set by employing pivots into the search space. We divide the feature space of character categ...
Yiping Yang, Ondrej Velek, Masaki Nakagawa
BMCBI
2010
121views more  BMCBI 2010»
13 years 2 months ago
A grammar-based distance metric enables fast and accurate clustering of large sets of 16S sequences
Background: We propose a sequence clustering algorithm and compare the partition quality and execution time of the proposed algorithm with those of a popular existing algorithm. T...
David J. Russell, Samuel F. Way, Andrew K. Benson,...
DMKD
1997
ACM
308views Data Mining» more  DMKD 1997»
13 years 9 months ago
A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining
Partitioning a large set of objects into homogeneous clusters is a fundamental operation in data mining. The k-means algorithm is best suited for implementing this operation becau...
Zhexue Huang
ICAIL
2005
ACM
13 years 10 months ago
Effective Document Clustering for Large Heterogeneous Law Firm Collections
Computational resources for research in legal environments have historically implied remote access to large databases of legal documents such as case law, statutes, law reviews an...
Jack G. Conrad, Khalid Al-Kofahi, Ying Zhao, Georg...