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Application of Trajectory Data Mining Techniques in CRM using Movement Based Community Clustering


V. Tanuja, P. Govindarajulu


Vol. 16  No. 11  pp. 20-29


Proliferation of location acquisition network technologies heightens very large trajectory data sets generation. Many data mining techniques have been proposed for efficient processing, managing and mining trajectory data. A trajectory is a sequence of geographical locations associated with timestamps. Day by day, trajectory data mining applications are increasing rapidly. Applications of trajectory data mining are ? movement behavior analysis of objects, people, vehicles, animals etc, and finding geographic locations, finding desired paths geographically and so on. Suffix-tree-like index data structure can be used for efficient management of trajectories. Customer Relationship Management (CRM) is vital in present competitive business scenario. CRM requires information about the customer community to elucidate the behavioral patterns of the customers. Grouping of customers according to business inputs plays an important role. The clustering technique of Data Mining is a useful tool to grouping. Present work proposed a trajectory based clustering technique to pick up the customer groups from the customer data. The data source will be the data of a public transportation organization.


Data mining, CRM, Trajectory data mining, Trajectory datasets, Trajectory clustering.