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PAKDD
2009
ACM
96views Data Mining» more  PAKDD 2009»
15 years 9 months ago
Aggregated Subset Mining
The usual data mining setting uses the full amount of data to derive patterns for different purposes. Taking cues from machine learning techniques, we explore ways to divide the d...
Albrecht Zimmermann, Björn Bringmann
ICDM
2007
IEEE
116views Data Mining» more  ICDM 2007»
15 years 9 months ago
Cross-Mining Binary and Numerical Attributes
We consider the problem of relating itemsets mined on binary attributes of a data set to numerical attributes of the same data. An example is biogeographical data, where the numer...
Gemma C. Garriga, Hannes Heikinheimo, Jouni K. Sep...
ICDM
2008
IEEE
156views Data Mining» more  ICDM 2008»
15 years 9 months ago
Mining Allocating Patterns in One-Sum Weighted Items
An Association Rule (AR) is a common knowledge model in data mining that describes an implicative cooccurring relationship between two disjoint sets of binary-valued transaction d...
Yanbo J. Wang, Xinwei Zheng, Frans Coenen, Cindy Y...
KDD
1998
ACM
107views Data Mining» more  KDD 1998»
15 years 7 months ago
Giga-Mining
Wedescribe an industrial-strength data mining application in telecommunications.Theapplication requires building a short (7 byte) profile for all telephonenumbersseen on a large t...
Corinna Cortes, Daryl Pregibon
KDD
1997
ACM
135views Data Mining» more  KDD 1997»
15 years 7 months ago
Brute-Force Mining of High-Confidence Classification Rules
This paper investigates a brute-force technique for mining classification rules from large data sets. We employ an association rule miner enhanced with new pruning strategies to c...
Roberto J. Bayardo Jr.