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PAKDD
2000
ACM
108views Data Mining» more  PAKDD 2000»
13 years 11 months ago
Association Rules
Tao Zhang
PAKDD
2000
ACM
140views Data Mining» more  PAKDD 2000»
13 years 11 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»
13 years 11 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»
13 years 11 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»
13 years 11 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»
13 years 11 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»
13 years 11 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