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DIS
2006
Springer
15 years 11 months ago
Symmetric Item Set Mining Based on Zero-Suppressed BDDs
In this paper, we propose a method for discovering hidden information from large-scale item set data based on the symmetry of items. Symmetry is a fundamental concept in the theory...
Shin-ichi Minato
CORR
2010
Springer
108views Education» more  CORR 2010»
15 years 7 months ago
Mining The Successful Binary Combinations: Methodology and A Simple Case Study
The importance of finding the characteristics leading to either a success or a failure is one of the driving forces of data mining. The various application areas of finding succes...
Yuval Cohen
KDD
2008
ACM
140views Data Mining» more  KDD 2008»
16 years 7 months ago
Semi-supervised approach to rapid and reliable labeling of large data sets
Supervised classification methods have been shown to be very effective for a large number of applications. They require a training data set whose instances are labeled to indicate...
György J. Simon, Vipin Kumar, Zhi-Li Zhang
KDD
2000
ACM
133views Data Mining» more  KDD 2000»
15 years 10 months ago
Data selection for support vector machine classifiers
The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classifier, is formulated as a concave minimiza...
Glenn Fung, Olvi L. Mangasarian
SDM
2007
SIAM
131views Data Mining» more  SDM 2007»
15 years 8 months ago
Load Shedding in Classifying Multi-Source Streaming Data: A Bayes Risk Approach
In many applications, we monitor data obtained from multiple streaming sources for collective decision making. The task presents several challenges. First, data in sensor networks...
Yijian Bai, Haixun Wang, Carlo Zaniolo