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Chieh-Ming Wu

dblp:64/170 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2011
0000-0003-3346-6395ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 2 first-authorDatabases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › pattern mining
association rule mining		 0.012002
Mining Generalized Association Rules Using Pruning Techniques · ICDM 2002
Data mining › pattern mining
frequent pattern mining		 0.012002
Mining Generalized Association Rules Using Pruning Techniques · ICDM 2002
Data mining › pattern mining › association rule mining
generalized association rules		 0.012002
Mining Generalized Association Rules Using Pruning Techniques · ICDM 2002

Methods — techniques the papers use, named apart from their topics

pruning · 0.0join methods · 0.0
YearPublicationVenuePosition
2011 Generalized association rule mining using an efficient data structure
Chieh-Ming Wu, Yin-Fu Huang
 Expert Syst. Appl.1
2011 A cost-efficient and versatile sanitizing algorithm by using a greedy approach
Chieh-Ming Wu, Yin-Fu Huang
 Soft Comput.1
2002 Mining Generalized Association Rules Using Pruning Techniques
abstract
The goal of the paper is to mine generalized association rules using pruning techniques. Given a large transaction database and a hierarchical taxonomy tree of the items, we try to find the association rules between the items at different levels in the taxonomy tree under the assumption that original frequent itemsets and association rules have already been generated beforehand In the proposed algorithm GMAR, we use join methods and pruning techniques to generate new generalized association rules. Through several comprehensive experiments, we find that the GMAR algorithm is much better than BASIC and Cumulate algorithms.
Yin-Fu Huang, Chieh-Ming Wu
 ICDM2