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Constructing user behavioral profile...

Gao, Wei.

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Constructing user behavioral profiles using data-mining-based approach.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Constructing user behavioral profiles using data-mining-based approach./
Author:	Gao, Wei.
Description:	187 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-06, Section: A, page: 2287.
Contained By:	Dissertation Abstracts International66-06A.
Subject:	Business Administration, Management. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3178418
ISBN:	0542179253

Constructing user behavioral profiles using data-mining-based approach.
Gao, Wei.

Constructing user behavioral profiles using data-mining-based approach. - 187 p.

Source: Dissertation Abstracts International, Volume: 66-06, Section: A, page: 2287.

Thesis (Ph.D.)--The University of Arizona, 2005.

User profiling has wide applications such as personalization, intrusion detection, and online customer analysis in e-business environments. In the past decade, most of past research on user profiling focused on factual profile construction and applications. A few researchers studied application-oriented behavioral profiling problems. In light of the advantages of behavioral profiles over their factual counterparts and the importance of fundamental understanding of them, this dissertation probes into the theoretical foundation, modeling and data-mining-based heuristic techniques for constructing behavioral profiles.

ISBN: 0542179253Subjects--Topical Terms:

626628
Business Administration, Management.

Constructing user behavioral profiles using data-mining-based approach.
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100 1 $a Gao, Wei. $3 1073960
245 1 0 $a Constructing user behavioral profiles using data-mining-based approach.
300 $a 187 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-06, Section: A, page: 2287.
500 $a Advisers: Olivia R. Liu Sheng; Daniel Zeng.
502 $a Thesis (Ph.D.)--The University of Arizona, 2005.
520 $a User profiling has wide applications such as personalization, intrusion detection, and online customer analysis in e-business environments. In the past decade, most of past research on user profiling focused on factual profile construction and applications. A few researchers studied application-oriented behavioral profiling problems. In light of the advantages of behavioral profiles over their factual counterparts and the importance of fundamental understanding of them, this dissertation probes into the theoretical foundation, modeling and data-mining-based heuristic techniques for constructing behavioral profiles.
520 $a We first propose a research framework for behavioral profiling and define the fundamentals. We build an optimization model for describing and solving a general type of behavioral profile construction problem. The analysis of the optimization model's analytic properties found a strong connection between the feasible solution to the model and the independent dominating set in a graph derived from the input of the model. Based on this finding, we employed two solution searching approaches: brute-force and Genetic Algorithm, and performed numerical analysis on a synthetic small-sized profiling problem. The results demonstrate the effectiveness of Genetic Algorithm for producing approximate optimal solution to the CH optimization problem.
520 $a We propose an innovative data-mining-based heuristic approach---hierarchical characteristic pattern mining to find solutions to the profile construction optimization problem. This approach builds behavioral profiles based on a new type of pattern---characteristic pattern and is appropriate for large-scale problems. Experiments using relatively large amounts of synthetic data were conducted to test the performance of this approach. The results show that the data-mining-based approach outperforms the Genetic Algorithm when the characteristic patterns exist.
520 $a Finally, a particular user behavioral profile application---web user identification is introduced to present problems and solutions when applying the data-mining-based behavioral profile construction approach into a real-world profile application. The experiments performed on a real-world dataset produced positive results of our approach in terms of effectiveness, efficiency, and interpretability.
520 $a The main contributions of the dissertation are: (1) proposing a comprehensive profiling research framework; (2) building an optimization model for solving a general type of profile construction problem; and (3) developing an innovative data-mining based heuristic approach to building behavioral profiles.
590 $a School code: 0009.
650 4 $a Business Administration, Management. $3 626628
650 4 $a Business Administration, Marketing. $3 1017573
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710 2 0 $a The University of Arizona. $3 1017508
773 0 $t Dissertation Abstracts International $g 66-06A.
790 1 0 $a Liu Sheng, Olivia R., $e advisor
790 1 0 $a Zeng, Daniel, $e advisor
790 $a 0009
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3178418