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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 for . This reduces the complexity of valid group pattern mining problem. We support the use of static method, which uses areas and instead to find group patterns thus reducing the complexity of the mining problem.

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 for . This reduces the complexity of valid group pattern mining problem. We support the use of static method, which uses areas and instead to find group patterns thus reducing the complexity of the mining problem.

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 for . This reduces the complexity of valid group pattern mining problem. We support the use of static method, which uses areas and instead to find group patterns thus reducing the complexity of the mining problem.

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 for . This reduces the complexity of valid group pattern mining problem. We support the use of static method, which uses areas and instead to find group patterns thus reducing the complexity of the mining problem.

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 for . This reduces the complexity of valid group pattern mining problem. We support the use of static method, which uses areas and instead to find group patterns thus reducing the complexity of the mining problem.

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 for . This reduces the complexity of valid group pattern mining problem. We support the use of static method, which uses areas and instead to find group patterns thus reducing the complexity of the mining problem.

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 for . This reduces the complexity of valid group pattern mining problem. We support the use of static method, which uses areas and instead to find group patterns thus reducing the complexity of the mining problem.