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HAIS
2009
Springer
14 years 9 days ago
Unsupervised Feature Selection in High Dimensional Spaces and Uncertainty
Developing models and methods to manage data vagueness is a current effervescent research field. Some work has been done with supervised problems but unsupervised problems and unce...
José Ramón Villar, María del ...
KDD
2002
ACM
106views Data Mining» more  KDD 2002»
14 years 8 months ago
Selecting the right interestingness measure for association patterns
Many techniques for association rule mining and feature selection require a suitable metric to capture the dependencies among variables in a data set. For example, metrics such as...
Pang-Ning Tan, Vipin Kumar, Jaideep Srivastava
KDD
2006
ACM
177views Data Mining» more  KDD 2006»
14 years 8 months ago
Topics over time: a non-Markov continuous-time model of topical trends
This paper presents an LDA-style topic model that captures not only the low-dimensional structure of data, but also how the structure changes over time. Unlike other recent work t...
Xuerui Wang, Andrew McCallum
KDD
2001
ACM
196views Data Mining» more  KDD 2001»
14 years 8 months ago
Efficient discovery of error-tolerant frequent itemsets in high dimensions
We present a generalization of frequent itemsets allowing the notion of errors in the itemset definition. We motivate the problem and present an efficient algorithm that identifie...
Cheng Yang, Usama M. Fayyad, Paul S. Bradley
CSE
2009
IEEE
14 years 2 months ago
Web Science 2.0: Identifying Trends through Semantic Social Network Analysis
—We introduce a novel set of social network analysis based algorithms for mining the Web, blogs, and online forums to identify trends and find the people launching these new tren...
Peter A. Gloor, Jonas Krauss, Stefan Nann, Kai Fis...