Paper Info
Title
Extracting Temporal Behavior Patterns of Mobile User
Abstract
Finding prevalent mobile user patterns in large amount of data has been one of the major problems in the area of mobile data mining. Particularly, the algorithms of discovering frequent user's behavior patterns in the mobile agent system have been studied extensively in recent years. The key feature in most of these algorithms is that they use a location log dataset with user’s requested services. In this case, some problems occur because they do not consider that mobile user's behavior patterns are dynamically variable as time passes. In this paper, we propose a novel data mining method based on temporal mobile access patterns that can efficiently discover mobile user's behavior patterns. An advantage of our method compactly stores user's behavior patterns according to location log dataset and requested services in memory. Thus, even the information datasets require large shared memory when they store, our approach still provides faster access and consume less memory than existing techniques.
Year
DOI
Venue
2008
10.1109/NCM.2008.192
NCM (2)
Keywords
Field
DocType
large shared memory,mobile user,method compactly stores user,location log dataset,frequent user,extracting temporal behavior patterns,temporal mobile access pattern,mobile data mining,behavior pattern,prevalent mobile user pattern,mobile agent system,association rules,databases,temporal,mining,shared memory,very large database,data mining,mobile computing,middleware,mobile communication
Mobile computing,Data mining,Behavioral pattern,Mobile search,Shared memory,Computer science,Mobile agent,Very large database,Association rule learning,Mobile telephony
Conference
Citations 
PageRank 
References 
5
0.58
11
Authors
4
Name
Order
Citations
PageRank
Seung-Cheol Lee1102.38
Eun-Ju Lee224727.70
Wongil Choi381.64
Ung Mo Kim412321.70