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» Rule Preference Effect in Associative Classification Mining
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CIKM
2006
Springer
14 years 1 months ago
Multi-evidence, multi-criteria, lazy associative document classification
We present a novel approach for classifying documents that combines different pieces of evidence (e.g., textual features of documents, links, and citations) transparently, through...
Adriano Veloso, Wagner Meira Jr., Marco Cristo, Ma...
ICDM
2003
IEEE
91views Data Mining» more  ICDM 2003»
14 years 3 months ago
MPIS: Maximal-Profit Item Selection with Cross-Selling Considerations
In the literature of data mining, many different algorithms for association rule mining have been proposed. However, there is relatively little study on how association rules can ...
Raymond Chi-Wing Wong, Ada Wai-Chee Fu, Ke Wang
SIGMOD
2005
ACM
161views Database» more  SIGMOD 2005»
14 years 10 months ago
Mining Top-k Covering Rule Groups for Gene Expression Data
In this paper, we propose a novel algorithm to discover the topk covering rule groups for each row of gene expression profiles. Several experiments on real bioinformatics datasets...
Gao Cong, Kian-Lee Tan, Anthony K. H. Tung, Xin Xu
KBS
2006
79views more  KBS 2006»
13 years 10 months ago
Using multiple and negative target rules to make classifiers more understandable
One major goal for data mining is to understand data. Rule based methods are better than other methods in making mining results comprehensible. However, the current rule based cla...
Jiuyong Li, Jason Jones
ICCS
2007
Springer
14 years 4 months ago
Pushing Frequency Constraint to Utility Mining Model
Traditional association rules mining (ARM) only concerns the frequency of itemsets, which may not bring large amount of profit. Utility mining only focuses on itemsets with high ut...
Jing Wang, Ying Liu, Lin Zhou, Yong Shi, Xingquan ...