Paper Info
Title
Discovering associations in high-dimensional data
Abstract
Association discovery is one of the most studied tasks in the field of data mining (Agrawal et al. 1993). It involves identifying items that occur together in data, and has numerous applications in manufacturing, commerce, administration and science. However, far more attention has been paid to how to discover associations than to what associations should be discovered. In this talk Geoff will provide a highly subjective tour of the field. He will • highlight shortcomings of the dominant frequent pattern paradigm; • illustrate benefits of the alternative top-k paradigm; and • present the self-sufficient itemsets approach to identifying potentially interesting associations as described in Webb & Zhang (2005), Webb (2007, 2008, 2010, 2011).
Year
Venue
Keywords
2012
ADC
alternative top-k paradigm,self-sufficient itemsets,data mining,high-dimensional data,association discovery,talk geoff,numerous application,dominant frequent pattern paradigm,discovering association,subjective tour,interesting association
Field
DocType
Citations 
Data science,Data mining,Clustering high-dimensional data,Computer science,Database,Zhàng
Conference
0
PageRank 
References 
Authors
0.34
6
1
Name
Order
Citations
PageRank
Geoffrey I. Webb13130234.10