Frequent sequential pattern mining for e-commerce applications

The objective of this project is to propose an improved web frequent sequential patterns mining algorithm for generating analysis rules for large-scale purchase datasets. By a careful evaluation of the traditional mining algorithms, we aim to design a better algorithm that is more scalable and effic...

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Bibliographic Details
Main Author:	Li, Xueheng.
Other Authors:	Kong, Hinny Pe Hin
Format:	Theses and Dissertations
Published:	2008
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Online Access:	http://hdl.handle.net/10356/4771
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Institution:	Nanyang Technological University

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record_format	dspace
spelling	sg-ntu-dr.10356-47712023-07-04T15:59:29Z Frequent sequential pattern mining for e-commerce applications Li, Xueheng. Kong, Hinny Pe Hin School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems The objective of this project is to propose an improved web frequent sequential patterns mining algorithm for generating analysis rules for large-scale purchase datasets. By a careful evaluation of the traditional mining algorithms, we aim to design a better algorithm that is more scalable and efficient. Master of Science (Communication and Network Systems) 2008-09-17T09:58:11Z 2008-09-17T09:58:11Z 2002 2002 Thesis http://hdl.handle.net/10356/4771 Nanyang Technological University application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
topic	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Li, Xueheng. Frequent sequential pattern mining for e-commerce applications
description	The objective of this project is to propose an improved web frequent sequential patterns mining algorithm for generating analysis rules for large-scale purchase datasets. By a careful evaluation of the traditional mining algorithms, we aim to design a better algorithm that is more scalable and efficient.
author2	Kong, Hinny Pe Hin
author_facet	Kong, Hinny Pe Hin Li, Xueheng.
format	Theses and Dissertations
author	Li, Xueheng.
author_sort	Li, Xueheng.
title	Frequent sequential pattern mining for e-commerce applications
title_short	Frequent sequential pattern mining for e-commerce applications
title_full	Frequent sequential pattern mining for e-commerce applications
title_fullStr	Frequent sequential pattern mining for e-commerce applications
title_full_unstemmed	Frequent sequential pattern mining for e-commerce applications
title_sort	frequent sequential pattern mining for e-commerce applications
publishDate	2008
url	http://hdl.handle.net/10356/4771
_version_	1772828972605767680

Frequent sequential pattern mining for e-commerce applications

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