Looking into the Seeds of Time: Discovering Temporal Patterns in Large Transaction Sets

This paper studies the problem of mining frequent itemsets along with their temporal patterns from large transaction sets. A model is proposed in which users define a large set of temporal patterns that are interesting or meaningful to them. A temporal pattern defines the set of time points where th...

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Main Authors: LI, Yingjiu, ZHU, Sencun, WANG, X. Sean, Jajodia, Sushil
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2006
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Online Access:https://ink.library.smu.edu.sg/sis_research/1094
http://dx.doi.org/10.1016/j.ins.2005.01.019
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spelling sg-smu-ink.sis_research-20932011-01-25T05:14:56Z Looking into the Seeds of Time: Discovering Temporal Patterns in Large Transaction Sets LI, Yingjiu ZHU, Sencun WANG, X. Sean Jajodia, Sushil This paper studies the problem of mining frequent itemsets along with their temporal patterns from large transaction sets. A model is proposed in which users define a large set of temporal patterns that are interesting or meaningful to them. A temporal pattern defines the set of time points where the user expects a discovered itemset to be frequent. The model is general in that (i) no constraints are placed on the interesting patterns given by the users, and (ii) two measures—inclusiveness and exclusiveness—are used to capture how well the temporal patterns match the time points given by the discovered itemsets. Intuitively, these measures indicate to what extent a discovered itemset is frequent at time points included in a temporal pattern p, but not at time points not in p. Using these two measures, one is able to model many temporal data mining problems appeared in the literature, as well as those that have not been studied. By exploiting the relationship within and between itemset space and pattern space simultaneously, a series of pruning techniques are developed to speed up the mining process. Experiments show that these pruning techniques allow one to obtain performance benefits up to 100 times over a direct extension of non-temporal data mining algorithms. 2006-01-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/1094 info:doi/10.1016/j.ins.2005.01.019 http://dx.doi.org/10.1016/j.ins.2005.01.019 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Knowledge discovery Temporal data mining Temporal pattern Information Security
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Knowledge discovery
Temporal data mining
Temporal pattern
Information Security
spellingShingle Knowledge discovery
Temporal data mining
Temporal pattern
Information Security
LI, Yingjiu
ZHU, Sencun
WANG, X. Sean
Jajodia, Sushil
Looking into the Seeds of Time: Discovering Temporal Patterns in Large Transaction Sets
description This paper studies the problem of mining frequent itemsets along with their temporal patterns from large transaction sets. A model is proposed in which users define a large set of temporal patterns that are interesting or meaningful to them. A temporal pattern defines the set of time points where the user expects a discovered itemset to be frequent. The model is general in that (i) no constraints are placed on the interesting patterns given by the users, and (ii) two measures—inclusiveness and exclusiveness—are used to capture how well the temporal patterns match the time points given by the discovered itemsets. Intuitively, these measures indicate to what extent a discovered itemset is frequent at time points included in a temporal pattern p, but not at time points not in p. Using these two measures, one is able to model many temporal data mining problems appeared in the literature, as well as those that have not been studied. By exploiting the relationship within and between itemset space and pattern space simultaneously, a series of pruning techniques are developed to speed up the mining process. Experiments show that these pruning techniques allow one to obtain performance benefits up to 100 times over a direct extension of non-temporal data mining algorithms.
format text
author LI, Yingjiu
ZHU, Sencun
WANG, X. Sean
Jajodia, Sushil
author_facet LI, Yingjiu
ZHU, Sencun
WANG, X. Sean
Jajodia, Sushil
author_sort LI, Yingjiu
title Looking into the Seeds of Time: Discovering Temporal Patterns in Large Transaction Sets
title_short Looking into the Seeds of Time: Discovering Temporal Patterns in Large Transaction Sets
title_full Looking into the Seeds of Time: Discovering Temporal Patterns in Large Transaction Sets
title_fullStr Looking into the Seeds of Time: Discovering Temporal Patterns in Large Transaction Sets
title_full_unstemmed Looking into the Seeds of Time: Discovering Temporal Patterns in Large Transaction Sets
title_sort looking into the seeds of time: discovering temporal patterns in large transaction sets
publisher Institutional Knowledge at Singapore Management University
publishDate 2006
url https://ink.library.smu.edu.sg/sis_research/1094
http://dx.doi.org/10.1016/j.ins.2005.01.019
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