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KDD
2006
ACM
109views Data Mining» more  KDD 2006»
14 years 9 months ago
Extracting redundancy-aware top-k patterns
Observed in many applications, there is a potential need of extracting a small set of frequent patterns having not only high significance but also low redundancy. The significance...
Dong Xin, Hong Cheng, Xifeng Yan, Jiawei Han
GFKL
2007
Springer
196views Data Mining» more  GFKL 2007»
14 years 3 months ago
Comparison of Recommender System Algorithms Focusing on the New-item and User-bias Problem
Recommender systems are used by an increasing number of e-commerce websites to help the customers to find suitable products from a large database. One of the most popular techniqu...
Stefan Hauger, Karen H. L. Tso, Lars Schmidt-Thiem...
DATAMINE
1999
108views more  DATAMINE 1999»
13 years 8 months ago
A Survey of Methods for Scaling Up Inductive Algorithms
Abstract. One of the de ning challenges for the KDD research community is to enable inductive learning algorithms to mine very large databases. This paper summarizes, categorizes, ...
Foster J. Provost, Venkateswarlu Kolluri
PREMI
2005
Springer
14 years 2 months ago
Geometric Decision Rules for Instance-Based Learning Problems
In the typical nonparametric approach to classification in instance-based learning and data mining, random data (the training set of patterns) are collected and used to design a d...
Binay K. Bhattacharya, Kaustav Mukherjee, Godfried...
DASFAA
2008
IEEE
190views Database» more  DASFAA 2008»
14 years 3 months ago
Analysis of Time Series Using Compact Model-Based Descriptions
Abstract. Recently, we have proposed a novel method for the compression of time series based on mathematical models that explore dependencies between different time series. This r...
Hans-Peter Kriegel, Peer Kröger, Alexey Pryak...