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» Divide-and-Conquer Strategies for Process Mining
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INCDM
2010
Springer
208views Data Mining» more  INCDM 2010»
13 years 9 months ago
Combining Unsupervised and Supervised Data Mining Techniques for Conducting Customer Portfolio Analysis
Abstract. Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies i...
Zhiyuan Yao, Annika H. Holmbom, Tomas Eklund, Barb...
ICDE
2004
IEEE
115views Database» more  ICDE 2004»
14 years 9 months ago
Go Green: Recycle and Reuse Frequent Patterns
In constrained data mining, users can specify constraints that can be used to prune the search space to avoid mining uninteresting knowledge. Since it is difficult to determine th...
Gao Cong, Beng Chin Ooi, Kian-Lee Tan, Anthony K. ...
SDM
2010
SIAM
181views Data Mining» more  SDM 2010»
13 years 5 months ago
Efficient Nonnegative Matrix Factorization with Random Projections
The recent years have witnessed a surge of interests in Nonnegative Matrix Factorization (NMF) in data mining and machine learning fields. Despite its elegant theory and empirical...
Fei Wang, Ping Li
ADMA
2010
Springer
248views Data Mining» more  ADMA 2010»
13 years 5 months ago
Classification Inductive Rule Learning with Negated Features
This paper reports on an investigation to compare a number of strategies to include negated features within the process of Inductive Rule Learning (IRL). The emphasis is on generat...
Stephanie Chua, Frans Coenen, Grant Malcolm
BNCOD
2003
127views Database» more  BNCOD 2003»
13 years 9 months ago
Performance Evaluation and Analysis of K-Way Join Variants for Association Rule Mining
Data mining aims at discovering important and previously unknown patterns from the dataset in the underlying database. Database mining performs mining directly on data stored in r...
P. Mishra, Sharma Chakravarthy