From sequential patterns to concurrent branch patterns: a new post sequential patterns mining approach

2.50
Hdl Handle:
http://hdl.handle.net/10547/556399
Title:
From sequential patterns to concurrent branch patterns: a new post sequential patterns mining approach
Authors:
Lu, Jing
Abstract:
Sequential patterns mining is an important pattern discovery technique used to identify frequently observed sequential occurrence of items across ordered transactions over time. It has been intensively studied and there exists a great diversity of algorithms. However, there is a major problem associated with the conventional sequential patterns mining in that patterns derived are often large and not very easy to understand or use. In addition, more complex relations among events are often hidden behind sequences. A novel model for sequential patterns called Sequential Patterns Graph (SPG) is proposed. The construction algorithm of SPG is presented with experimental results to substantiate the concept. The thesis then sets out to define some new structural patterns such as concurrent branch patterns, exclusive patterns and iterative patterns which are generally hidden behind sequential patterns. Finally, an integrative framework, named Post Sequential Patterns Mining (PSPM), which is based on sequential patterns mining, is also proposed for the discovery and visualisation of structural patterns. This thesis is intended to prove that discrete sequential patterns derived from traditional sequential patterns mining can be modelled graphically using SPG. It is concluded from experiments and theoretical studies that SPG is not only a minimal representation of sequential patterns mining, but it also represents the interrelation among patterns and establishes further the foundation for mining structural knowledge (i.e. concurrent branch patterns, exclusive patterns and iterative patterns). from experiments conducted on both synthetic and real datasets, it is shown that Concurrent Branch Patterns (CBP) mining is an effective and efficient mining algorithm suitable for concurrent branch patterns.
Citation:
Lu, J. (2006) 'From sequential patterns to concurrent branch patterns: a new post sequential patterns mining approach'. PhD thesis. University of Bedfordshire.
Publisher:
University of Bedfordshire
Issue Date:
Oct-2006
URI:
http://hdl.handle.net/10547/556399
Type:
Thesis or dissertation
Language:
en
Description:
A thesis submitted for the degree of Doctor ofPhilosophy of the University of Bedfordshire
Appears in Collections:
PhD e-theses

Full metadata record

DC FieldValue Language
dc.contributor.authorLu, Jingen
dc.date.accessioned2015-06-04T12:40:24Zen
dc.date.available2015-06-04T12:40:24Zen
dc.date.issued2006-10en
dc.identifier.citationLu, J. (2006) 'From sequential patterns to concurrent branch patterns: a new post sequential patterns mining approach'. PhD thesis. University of Bedfordshire.en
dc.identifier.urihttp://hdl.handle.net/10547/556399en
dc.descriptionA thesis submitted for the degree of Doctor ofPhilosophy of the University of Bedfordshireen
dc.description.abstractSequential patterns mining is an important pattern discovery technique used to identify frequently observed sequential occurrence of items across ordered transactions over time. It has been intensively studied and there exists a great diversity of algorithms. However, there is a major problem associated with the conventional sequential patterns mining in that patterns derived are often large and not very easy to understand or use. In addition, more complex relations among events are often hidden behind sequences. A novel model for sequential patterns called Sequential Patterns Graph (SPG) is proposed. The construction algorithm of SPG is presented with experimental results to substantiate the concept. The thesis then sets out to define some new structural patterns such as concurrent branch patterns, exclusive patterns and iterative patterns which are generally hidden behind sequential patterns. Finally, an integrative framework, named Post Sequential Patterns Mining (PSPM), which is based on sequential patterns mining, is also proposed for the discovery and visualisation of structural patterns. This thesis is intended to prove that discrete sequential patterns derived from traditional sequential patterns mining can be modelled graphically using SPG. It is concluded from experiments and theoretical studies that SPG is not only a minimal representation of sequential patterns mining, but it also represents the interrelation among patterns and establishes further the foundation for mining structural knowledge (i.e. concurrent branch patterns, exclusive patterns and iterative patterns). from experiments conducted on both synthetic and real datasets, it is shown that Concurrent Branch Patterns (CBP) mining is an effective and efficient mining algorithm suitable for concurrent branch patterns.en
dc.language.isoenen
dc.publisherUniversity of Bedfordshireen
dc.subjectG790 Artificial Intelligence not elsewhere classifieden
dc.subjectsequential patterns miningen
dc.subjectSequential Patterns Graphen
dc.subjectpattern discoveryen
dc.subjectpattern recognitionen
dc.titleFrom sequential patterns to concurrent branch patterns: a new post sequential patterns mining approachen
dc.typeThesis or dissertationen
dc.type.qualificationnamePhDen_GB
dc.type.qualificationlevelPhDen
dc.publisher.institutionUniversity of Bedfordshireen
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