Efficient data mining for calling path patterns in GSM networks

Lee, Anthony J. T.; Wang, Yao-Te

doi:10.1016/s0306-4379(02)00112-6

Cited by 18 publications

(11 citation statements)

References 17 publications

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“…The experiment data of synthetic movement paths were created by path construction, as described by Lee and Wang [6], and were annotated with periodicity parameters, as described by Özden et al [8]. Table 4 lists the parameters and their default values for the generation of movement paths.…”

Section: Resultsmentioning

confidence: 99%

“…The structure of telecommunications networks is not taken into account when the sequential patterns are mined; hence, too many candidate sequences which will never become frequent, are created and tested. Moreover, the movement trends of mobile phone users can be forecast more efficiently by potential maximal frequent calling path patterns than by sequential patterns [6].…”

Section: Related Workmentioning

confidence: 99%

“…It is effective to predict movement trends with the observed mobile movement patterns instead of sequential patterns [6], and in this study the proposed mining approach is highly efficient at discovering periodic movement patterns.…”

Section: Related Workmentioning

confidence: 99%

“…In light of such trends, a method that could identify trends in mobile service subscribers' movements while they make calls on their mobile phones would enable telecommunications operators to make arrangements and deploy relevant system resources and information sought by these users at prior [6,9]. In addition to improving the operational performance of the entire communication system, such a scheme would enhance the ability of service providers to offer personalized services (e.g., deployment of base stations, hands-off strategies, dispatch of user access data, pricing strategies, advertising and marketing and behavior analysis).…”

Section: Introductionmentioning

confidence: 99%

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Mining periodic movement patterns of mobile phone users based on an efficient sampling approach

Wang

Cheng

2009

Appl Intell

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In m-commerce services, the periodic movement trends of customers at specific periods can be adopted to allocate the resources of telecommunications systems effectively and offer personalized location-based services. This study explores the mining of periodic maximal promising movement patterns. A detailed process for mining periodic maximal promising movement patterns based on graph mapping and sampling techniques is devised to enhance mining efficiency. First, a random sample of movement paths from time intervals is taken. Second, a unique path graph structure is built to store the movement paths obtained from the sample. Third, a graph traversal algorithm is developed to identify the maximal promising movement patterns. Finally, vector operations are undertaken to examine the maximal promising movement patterns in order to derive the periodic maximal promising movement patterns. Experimental results reveal that the sampling approach with mining has excellent execution efficiency and scalability in the investigation of periodic maximal promising movement patterns.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mining periodic movement patterns of mobile phone users based on an efficient sampling approach

Wang

Cheng

2009

Appl Intell

Self Cite

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“…Algorithms for mining sequential patterns are common in Web navigation pattern mining (Agrawal & Srikant, 1995;Lee & Wang, 2003;Lin & Lee, 2005;Masseglia, Poncelet, & Teisseire, 2009;Pei et al, 2004). Apriori algorithm (Agrawal & Srikant, 1995) introduces a level-wise iterative search to discover all maximal frequent sequential patterns.…”

Section: Related Workmentioning

confidence: 99%