Title :
A fusion of data mining techniques for predicting movement of mobile users
Author :
Duong, Thuy Van T. ; Dinh Que Tran
Author_Institution :
Center for Appl. Inf. Technol., Ton DucThang Univ., Vietnam
Abstract :
Predicting locations of users with portable devices such as IP phones, smart-phones, iPads and iPods in public wireless local area networks (WLANs) plays a crucial role in location management and network resource allocation. Many techniques in machine learning and data mining, such as sequential pattern mining and clustering, have been widely used. However, these approaches have two deficiencies. First, because they are based on profiles of individual mobility behaviors, a sequential pattern technique may fail to predict new users or users with movement on novel paths. Second, using similar mobility behaviors in a cluster for predicting the movement of users may cause significant degradation in accuracy owing to indistinguishable regular movement and random movement. In this paper, we propose a novel fusion technique that utilizes mobility rules discovered from multiple similar users by combining clustering and sequential pattern mining. The proposed technique with two algorithms, named the clustering-based-sequential-pattern-mining (CSPM) and sequential-pattern-mining-based-clustering (SPMC), can deal with the lack of information in a personal profile and avoid some noise due to random movements by users. Experimental results show that our approach outperforms existing approaches in terms of efficiency and prediction accuracy.
Keywords :
data mining; mobile computing; pattern clustering; CSPM; SPMC; WLAN; clustering-based-sequential-pattern-mining; data mining techniques; location management; machine learning; mobile users; network resource allocation; public wireless local area networks; random movement; regular movement; sequential-pattern-mining-based-clustering; similar mobility behaviors; Data mining; Hidden Markov models; History; Mobile communication; Mobile computing; Predictive models; Wireless LAN; Clustering; mobile user; mobility pattern; movement prediction; sequential pattern;
Journal_Title :
Communications and Networks, Journal of
DOI :
10.1109/JCN.2015.000104