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ICDM
2005
IEEE
135views Data Mining» more  ICDM 2005»
15 years 9 months ago
Bit Reduction Support Vector Machine
Abstract— Support vector machines are very accurate classifiers and have been widely used in many applications. However, the training and to a lesser extent prediction time of s...
Tong Luo, Lawrence O. Hall, Dmitry B. Goldgof, And...
138
Voted
IPPS
2000
IEEE
15 years 8 months ago
Scalable Parallel Clustering for Data Mining on Multicomputers
This paper describes the design and implementation on MIMD parallel machines of P-AutoClass, a parallel version of the AutoClass system based upon the Bayesian method for determini...
D. Foti, D. Lipari, Clara Pizzuti, Domenico Talia
INCDM
2010
Springer
208views Data Mining» more  INCDM 2010»
15 years 5 months ago
Combining Unsupervised and Supervised Data Mining Techniques for Conducting Customer Portfolio Analysis
Abstract. Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies i...
Zhiyuan Yao, Annika H. Holmbom, Tomas Eklund, Barb...
EDM
2009
125views Data Mining» more  EDM 2009»
15 years 1 months ago
A Data Mining Approach to Reveal Representative Collaboration Indicators in Open Collaboration Frameworks
Data mining methods are successful in educational environments to discover new knowledge or learner skills or features. Unfortunately, they have not been used in depth with collabo...
Antonio R. Anaya, Jesus Boticario
118
Voted
PAKDD
2009
ACM
87views Data Mining» more  PAKDD 2009»
15 years 10 months ago
Application-Independent Feature Construction from Noisy Samples
When training classifiers, presence of noise can severely harm their performance. In this paper, we focus on “non-class” attribute noise and we consider how a frequent fault-t...
Dominique Gay, Nazha Selmaoui, Jean-Françoi...