SGPM: Static Group Pattern Mining using apriori-like sliding window

SGPM: Static Group Pattern Mining using apriori-like sliding window

Mobile user data mining is a field that focuses on extracting interesting pattern and knowledge out from data generated by mobile users. Group pattern is a type of mobile user data mining method. In group pattern mining, group patterns from a given user movement database is found based on spatio-tem...

Full description

Saved in:
Bibliographic Details
Main Authors: GOH, John, Taniar, David, LIM, Ee Peng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2006
Subjects:
Static method
User behavior
Mobile computing
Sliding window
Localization
Database
Data type
Knowledge engineering
Data field
Information extraction
Data analysis
Data mining
Knowledge discovery
Databases and Information Systems
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/895
https://ink.library.smu.edu.sg/context/sis_research/article/1894/viewcontent/Goh2006_Chapter_SGPMStaticGroupPatternMiningUs.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
Description
Summary:Mobile user data mining is a field that focuses on extracting interesting pattern and knowledge out from data generated by mobile users. Group pattern is a type of mobile user data mining method. In group pattern mining, group patterns from a given user movement database is found based on spatio-temporal distances. In this paper, we propose an improvement of efficiency using area method for locating mobile users and using sliding window for static group pattern mining. This reduces the complexity of valid group pattern mining problem. We support the use of static method, which uses areas and sliding windows instead to find group patterns thus reducing the complexity of the mining problem.

Similar Items