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IPPS
1999
IEEE
14 years 1 months ago
Parallel Out-of-Core Divide-and-Conquer Techniques with Application to Classification Trees
Classification is an important problem in the field of data mining. Construction of good classifiers is computationally intensive and offers plenty of scope for parallelization. D...
Mahesh K. Sreenivas, Khaled Alsabti, Sanjay Ranka
KDD
1998
ACM
118views Data Mining» more  KDD 1998»
14 years 28 days ago
A Belief-Driven Method for Discovering Unexpected Patterns
Several pattern discovery methods proposed in the data mining literature have the drawbacks that they discover too many obvious or irrelevant patterns and that they do not leverag...
Balaji Padmanabhan, Alexander Tuzhilin
KDD
2000
ACM
97views Data Mining» more  KDD 2000»
14 years 8 days ago
Towards an effective cooperation of the user and the computer for classification
Decision trees have been successfully used for the task of classification. However, state-of-the-art algorithms do not incorporate the user in the tree construction process. This ...
Mihael Ankerst, Martin Ester, Hans-Peter Kriegel
AUSDM
2006
Springer
122views Data Mining» more  AUSDM 2006»
14 years 13 days ago
Analysis of Breast Feeding Data Using Data Mining Methods
The purpose of this study is to demonstrate the benefit of using common data mining techniques on survey data where statistical analysis is routinely applied. The statistical surv...
Hongxing He, Huidong Jin, Jie Chen, Damien McAulla...
KDD
2004
ACM
330views Data Mining» more  KDD 2004»
14 years 9 months ago
Learning to detect malicious executables in the wild
In this paper, we describe the development of a fielded application for detecting malicious executables in the wild. We gathered 1971 benign and 1651 malicious executables and enc...
Jeremy Z. Kolter, Marcus A. Maloof