A partition based method for finding highly correlated pairs
type: Item
instance of: scholarly article
title: A partition based method for finding highly correlated pairs
zbMATH Open document ID: 1209.68192
DOI: 10.1504/IJDMMM.2010.035562
author: Shuxin Li
author: Sheau-Dong Lang
published in: International Journal of Data Mining, Modelling and Management (IJDMMM)
publication date: 2011-01-06T00:00:00Z
review text: Summary: The problem of finding highly correlated pairs is to output all item pairs whose (Pearson) correlation coefficients are greater than a user-specified correlation threshold. Effective discovery of such item pairs is of primary importance in many real data mining applications. Algorithm and Taper algorithm are special cases of our new algorithm with respect to the number of segments. Experimental results on real datasets demonstrate the feasibility and superiority of our algorithm. Recently, the Taper algorithm is developed to discover the set of highly correlated item pairs. In this paper, we present a generalised Taper algorithm to find strongly correlated pairs between items by partitioning the collection of transactions into different segments, so as to achieve better pruning effect and less running time. Consequently, it can be proved that both are naive.
Mathematics Subject Classification ID: 68P15
Mathematics Subject Classification ID: 68P05
zbMATH DE Number: 5832979
zbMATH Keywords: correlation
zbMATH Keywords: association rules
zbMATH Keywords: Pearson correlation coefficients
zbMATH Keywords: transactional databases
zbMATH Keywords: data mining
zbMATH Keywords: partition
zbMATH Keywords: highly correlated pairs
MaRDI profile type: Publication
full work available at URL: ijdmmm.2010.035562
OpenAlex ID: W2045260335
description: scientific article; zbMATH DE number 5832979