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PAKDD
2000
ACM
108views Data Mining» more  PAKDD 2000»
14 years 3 months ago
Association Rules
Tao Zhang
PAKDD
2000
ACM
140views Data Mining» more  PAKDD 2000»
14 years 3 months ago
Performance Controlled Data Reduction for Knowledge Discovery in Distributed Databases
The objective of data reduction is to obtain a compact representation of a large data set to facilitate repeated use of non-redundant information with complex and slow learning alg...
Slobodan Vucetic, Zoran Obradovic
PAKDD
2000
ACM
100views Data Mining» more  PAKDD 2000»
14 years 3 months ago
Discovery of Relevant Weights by Minimizing Cross-Validation Error
In order to discover relevant weights of neural networks, this paper proposes a novel method to learn a distinct squared penalty factor for each weight as a minimization problem ov...
Kazumi Saito, Ryohei Nakano
PAKDD
2000
ACM
128views Data Mining» more  PAKDD 2000»
14 years 3 months ago
Efficient Detection of Local Interactions in the Cascade Model
Detection of interactions among data items constitutes an essential part of knowledge discovery. The cascade model is a rule induction methodology using levelwise expansion of a la...
Takashi Okada
PAKDD
2000
ACM
161views Data Mining» more  PAKDD 2000»
14 years 3 months ago
Adaptive Boosting for Spatial Functions with Unstable Driving Attributes
Combining multiple global models (e.g. back-propagation based neural networks) is an effective technique for improving classification accuracy by reducing a variance through manipu...
Aleksandar Lazarevic, Tim Fiez, Zoran Obradovic
PAKDD
2000
ACM
128views Data Mining» more  PAKDD 2000»
14 years 3 months ago
A Comparative Study of Classification Based Personal E-mail Filtering
This paper addresses personal E-mail filtering by casting it in the framework of text classification. Modeled as semi-structured documents, Email messages consist of a set of field...
Yanlei Diao, Hongjun Lu, Dekai Wu
PAKDD
2000
ACM
124views Data Mining» more  PAKDD 2000»
14 years 3 months ago
Feature Selection for Clustering
In clustering, global feature selection algorithms attempt to select a common feature subset that is relevant to all clusters. Consequently, they are not able to identify individu...
Manoranjan Dash, Huan Liu