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ICDM
2006
IEEE
113views Data Mining» more  ICDM 2006»
9 years 5 months ago
Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints
In frequent geographic pattern mining a large amount of patterns is well known a priori. This paper presents a novel approach for mining frequent geographic patterns without assoc...
Vania Bogorny, João Francisco Valiati, Sand...
ICDM
2006
IEEE
95views Data Mining» more  ICDM 2006»
9 years 5 months ago
TOP-COP: Mining TOP-K Strongly Correlated Pairs in Large Databases
Recently, there has been considerable interest in computing strongly correlated pairs in large databases. Most previous studies require the specification of a minimum correlation...
Hui Xiong, Mark Brodie, Sheng Ma
ICDM
2006
IEEE
137views Data Mining» more  ICDM 2006»
9 years 5 months ago
Mining Complex Time-Series Data by Learning Markovian Models
In this paper, we propose a novel and general approach for time-series data mining. As an alternative to traditional ways of designing specific algorithm to mine certain kind of ...
Yi Wang, Lizhu Zhou, Jianhua Feng, Jianyong Wang, ...
ICDM
2006
IEEE
130views Data Mining» more  ICDM 2006»
9 years 5 months ago
A Framework for Regional Association Rule Mining in Spatial Datasets
The immense explosion of geographically referenced data calls for efficient discovery of spatial knowledge. One critical requirement for spatial data mining is the capability to ...
Wei Ding 0003, Christoph F. Eick, Jing Wang 0007, ...
ICDM
2006
IEEE
116views Data Mining» more  ICDM 2006»
9 years 5 months ago
Improving Personalization Solutions through Optimal Segmentation of Customer Bases
On the Web, where the search costs are low and the competition is just a mouse click away, it is crucial to segment the customers intelligently in order to offer more targeted and...
Tianyi Jiang, Alexander Tuzhilin
ICDM
2006
IEEE
91views Data Mining» more  ICDM 2006»
9 years 5 months ago
Entropy-based Concept Shift Detection
When monitoring sensory data (e.g., from a wearable device) the context oftentimes changes abruptly: people move from one situation (e.g., working quietly in their office) to ano...
Peter Vorburger, Abraham Bernstein
ICDM
2006
IEEE
98views Data Mining» more  ICDM 2006»
9 years 5 months ago
What is the Dimension of Your Binary Data?
Many 0/1 datasets have a very large number of variables; however, they are sparse and the dependency structure of the variables is simpler than the number of variables would sugge...
Nikolaj Tatti, Taneli Mielikäinen, Aristides ...
ICDM
2006
IEEE
133views Data Mining» more  ICDM 2006»
9 years 5 months ago
TRIAS - An Algorithm for Mining Iceberg Tri-Lattices
In this paper, we present the foundations for mining frequent tri-concepts, which extend the notion of closed itemsets to three-dimensional data to allow for mining folksonomies. ...
Robert Jäschke, Andreas Hotho, Christoph Schm...
ICDM
2006
IEEE
151views Data Mining» more  ICDM 2006»
9 years 5 months ago
Decision Trees for Functional Variables
Classification problems with functionally structured input variables arise naturally in many applications. In a clinical domain, for example, input variables could include a time...
Suhrid Balakrishnan, David Madigan
ICDM
2006
IEEE
84views Data Mining» more  ICDM 2006»
9 years 5 months ago
Exploratory Under-Sampling for Class-Imbalance Learning
Under-sampling is a class-imbalance learning method which uses only a subset of major class examples and thus is very efficient. The main deficiency is that many major class exa...
Xu-Ying Liu, Jianxin Wu, Zhi-Hua Zhou
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