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ICDE
2007
IEEE
179views Database» more  ICDE 2007»
14 years 4 months ago
A New ILP-based Concept Discovery Method for Business Intelligence
In this work, we propose a multi-relational concept discovery method for business intelligence applications. Multi-relational data mining finds interesting patterns that span ove...
Seda Daglar Toprak, Pinar Senkul, Yusuf Kavurucu, ...
CLEF
2010
Springer
13 years 11 months ago
Detection of Visual Concepts and Annotation of Images Using Predictive Clustering Trees
Abstract. In this paper, we present a multiple targets classification system for visual concepts detection and image annotation. Multiple targets classification (MTC) is a variant ...
Ivica Dimitrovski, Dragi Kocev, Suzana Loskovska, ...
INFORMATICALT
2008
196views more  INFORMATICALT 2008»
13 years 10 months ago
An Efficient and Sensitive Decision Tree Approach to Mining Concept-Drifting Data Streams
Abstract. Data stream mining has become a novel research topic of growing interest in knowledge discovery. Most proposed algorithms for data stream mining assume that each data blo...
Cheng-Jung Tsai, Chien-I Lee, Wei-Pang Yang
ICMCS
2009
IEEE
131views Multimedia» more  ICMCS 2009»
13 years 7 months ago
Web image mining using concept sensitive Markov stationary features
With the explosive growth of web resources, how to mine semantically relevant images efficiently becomes a challenging and necessary task. In this paper, we propose a concept sens...
Chunjie Zhang, Jing Liu, Hanqing Lu, Songde Ma
ICDM
2008
IEEE
145views Data Mining» more  ICDM 2008»
14 years 4 months ago
Paired Learners for Concept Drift
To cope with concept drift, we paired a stable online learner with a reactive one. A stable learner predicts based on all of its experience, whereas a reactive learner predicts ba...
Stephen H. Bach, Marcus A. Maloof