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Author Koufakou, A. ♦ Ragothaman, P.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2011
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Itemsets ♦ Clustering algorithms ♦ Runtime ♦ Association rules ♦ Face ♦ Noise ♦ skewed support distribution ♦ hyperclique ♦ non-derivable itemset ♦ frequent itemset mining ♦ dense data
Abstract Hyper cliques have been successfully applied in a number of applications, e.g. clustering and noise removal. A hyper clique is an item set containing items that are strongly correlated with each other. Even though hyper cliques have been shown to handle datasets with skewed support distribution and low support threshold well, they might still face problems for dense datasets and lower h-confidence threshold. In this paper, we propose a new pruning method based on combining hyper cliques and Non-Derivable Item sets (NDIs) in order to substantially reduce the amount of generated hyper clique sets. Specifically, we propose a new collection of hyper cliques, called Non-Derivable Hyper cliques (NDHC), and present an efficient algorithm to mine these sets, called NDHC Miner. The proposed NDHC collection is a loss less representation of hyper cliques, i.e., given the item sets in NDHC, we can generate the complete collection of hyper cliques and their support, without additional scanning of the dataset. We experimentally compare NDHC with Hyper cliques (HC), as well as another condensed representation of hyper cliques, maximal hyper cliques (MHP). Our experiments show that the NDHC collection offers substantial advantages over HC, and even MHP, especially for dense datasets and lower h-confidence threshold values.
ISBN 9781457720680
ISSN 10823409
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2011-11-07
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9780769545967
Size (in Bytes) 212.66 kB
Page Count 8
Starting Page 489
Ending Page 496


Source: IEEE Xplore Digital Library