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PKDD
2004
Springer
141views Data Mining» more  PKDD 2004»
14 years 4 months 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
KBS
2006
79views more  KBS 2006»
13 years 11 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
AUSDM
2007
Springer
84views Data Mining» more  AUSDM 2007»
14 years 5 months ago
Detecting Anomalous Longitudinal Associations Through Higher Order Mining
The detection of unusual or anomalous data is an important function in automated data analysis or data mining. However, the diversity of anomaly detection algorithms shows that it...
Ping Liang, John F. Roddick
ICDM
2003
IEEE
91views Data Mining» more  ICDM 2003»
14 years 4 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
SPAA
1997
ACM
14 years 3 months ago
A Localized Algorithm for Parallel Association Mining
Discovery of association rules is an important database mining problem. Mining for association rules involves extracting patterns from large databases and inferring useful rules f...
Mohammed Javeed Zaki, Srinivasan Parthasarathy, We...