Paper Info
Title
Randomization Methods for Assessing the Significance of Data Mining Results
Abstract
Data mining research has developed many algorithms for various analysis tasks on large and complex datasets. However, assessing the significance of data mining results has received less attention. Analytical methods are rarely available, and hence one has to use computationally intensive methods. Randomization approaches based on null models provide, at least in principle, a general approach that can be used to obtain empirical p-values for various types of data mining approaches. I review some of the recent work in this area, outlining some of the open questions and problems.
Year
DOI
Venue
2009
10.1007/978-3-642-04125-9_1
ISMIS
Keywords
Field
DocType
empirical p-values,various analysis task,data mining research,complex datasets,various type,analytical method,data mining approach,data mining results,computationally intensive method,general approach,randomization methods,data mining result,null model,data mining
Data mining,Computer science,Randomization,Data type,Artificial intelligence,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
1
Authors
1
Name
Order
Citations
PageRank
Heikki Mannila165951495.69