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» Discovering Classification from Data of Multiple Sources
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KDD
2004
ACM
164views Data Mining» more  KDD 2004»
14 years 8 months ago
Ordering patterns by combining opinions from multiple sources
Pattern ordering is an important task in data mining because the number of patterns extracted by standard data mining algorithms often exceeds our capacity to manually analyze the...
Pang-Ning Tan, Rong Jin
PKDD
2004
Springer
141views Data Mining» more  PKDD 2004»
14 years 25 days ago
Spatial Associative Classification at Different Levels of Granularity: A Probabilistic Approach
In this paper we propose a novel spatial associative classifier method based on a multi-relational approach that takes spatial relations into account. Classification is driven by s...
Michelangelo Ceci, Annalisa Appice, Donato Malerba
KDD
2009
ACM
168views Data Mining» more  KDD 2009»
14 years 8 months ago
Name-ethnicity classification from open sources
The problem of ethnicity identification from names has a variety of important applications, including biomedical research, demographic studies, and marketing. Here we report on th...
Anurag Ambekar, Charles B. Ward, Jahangir Mohammed...
KDD
1997
ACM
96views Data Mining» more  KDD 1997»
13 years 11 months ago
Using General Impressions to Analyze Discovered Classification Rules
One of the important problems in data mining is the evaluation of subjective interestingness of the discovered rules. Past research has found that in many real-life applications i...
Bing Liu, Wynne Hsu, Shu Chen
IAT
2008
IEEE
14 years 1 months ago
Discovering and Modelling Multiple Interests of Users in Collaborative Tagging Systems
We analyse data obtained from several collaborative tagging systems and discover that user interests can be very diverse. Traditional methods for representing interests of users a...
Ching-man Au Yeung, Nicholas Gibbins, Nigel Shadbo...