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ICML
2004
IEEE
16 years 2 months ago
K-means clustering via principal component analysis
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
Chris H. Q. Ding, Xiaofeng He
GECCO
2004
Springer
127views Optimization» more  GECCO 2004»
15 years 6 months ago
Improved Niching and Encoding Strategies for Clustering Noisy Data Sets
Clustering is crucial to many applications in pattern recognition, data mining, and machine learning. Evolutionary techniques have been used with success in clustering, but most su...
Olfa Nasraoui, Elizabeth Leon
EUROPAR
2007
Springer
15 years 7 months ago
Parallel Nearest Neighbour Algorithms for Text Categorization
In this paper we describe the parallelization of two nearest neighbour classification algorithms. Nearest neighbour methods are well-known machine learning techniques. They have be...
Reynaldo Gil-García, José Manuel Bad...
ICML
2009
IEEE
15 years 8 months ago
A novel lexicalized HMM-based learning framework for web opinion mining
Merchants selling products on the Web often ask their customers to share their opinions and hands-on experiences on products they have purchased. As e-commerce is becoming more an...
Wei Jin, Hung Hay Ho
ADBIS
1999
Springer
104views Database» more  ADBIS 1999»
15 years 5 months ago
Arbiter Meta-Learning with Dynamic Selection of Classifiers and Its Experimental Investigation
In data mining, the selection of an appropriate classifier to estimate the value of an unknown attribute for a new instance has an essential impact to the quality of the classifica...
Alexey Tsymbal, Seppo Puuronen, Vagan Y. Terziyan