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KDD
2009
ACM
239views Data Mining» more  KDD 2009»
16 years 4 months ago
Tell me something I don't know: randomization strategies for iterative data mining
There is a wide variety of data mining methods available, and it is generally useful in exploratory data analysis to use many different methods for the same dataset. This, however...
Heikki Mannila, Kai Puolamäki, Markus Ojala, ...
KDD
2009
ACM
188views Data Mining» more  KDD 2009»
16 years 4 months ago
Mining discrete patterns via binary matrix factorization
Mining discrete patterns in binary data is important for subsampling, compression, and clustering. We consider rankone binary matrix approximations that identify the dominant patt...
Bao-Hong Shen, Shuiwang Ji, Jieping Ye
KDD
2009
ACM
159views Data Mining» more  KDD 2009»
16 years 4 months ago
Mining brain region connectivity for alzheimer's disease study via sparse inverse covariance estimation
Effective diagnosis of Alzheimer's disease (AD), the most common type of dementia in elderly patients, is of primary importance in biomedical research. Recent studies have de...
Liang Sun, Rinkal Patel, Jun Liu, Kewei Chen, Tere...
KDD
2009
ACM
152views Data Mining» more  KDD 2009»
16 years 4 months ago
A multi-relational approach to spatial classification
Spatial classification is the task of learning models to predict class labels based on the features of entities as well as the spatial relationships to other entities and their fe...
Richard Frank, Martin Ester, Arno Knobbe
KDD
2009
ACM
232views Data Mining» more  KDD 2009»
16 years 4 months ago
Classification of software behaviors for failure detection: a discriminative pattern mining approach
Software is a ubiquitous component of our daily life. We often depend on the correct working of software systems. Due to the difficulty and complexity of software systems, bugs an...
David Lo, Hong Cheng, Jiawei Han, Siau-Cheng Khoo,...
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