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Density-based Place Clustering Using Geo-Social Network Data

Density-based Place Clustering Using Geo-Social Network Data


Spatial clustering deals with the unsupervised grouping of places into clusters and finds important applications in urban planning and marketing. Current spatial clustering models disregard information about the people and the time who and when are related to the clustered places. In this paper, we show how the density-based clustering paradigm can be extended to apply on places which are visited by users of a geo-social network. Our model considers spatio-temporal information and the social relationships between users who visit the clustered places. After formally defining the model and the distance measure it relies on, we provide alternatives to our model and the distance measure. We evaluate the effectiveness of our model via a case study on real data; in addition, we design two quantitative measures, called social entropy and community score to evaluate the quality of the discovered clusters. The results show that temporal-geo-social clusters have special properties and cannot be found by applying simple spatial clustering approaches and other alternatives.

Existing System:

The time of the check-ins made by users is not considered during the clustering process. The social connections established between places may be either out-dated or based on distant checkins in terms of time. For instance, the place clusters discovered according to the checkins one year ago may not interest analysts that value recent relationships between places. In addition, a cluster may have low quality if the checkin times of the different places in it vary significantly.

Proposed System:

We propose and formulate the problem of density-based clustering GeoSN places. We define a simple but effective social distance measure between places in GeoSNs. Y We demonstrate the effectiveness of DCPGS by case studies and quantitative evaluation through two quality measures that are also devised in this paper. The rest of the paper is organized as follows. Section 2 formulates the DCPGS problem, defines the social distance measure between places that we use, and introduces three methods of incorporating temporal information into DCPGS.

We study three ways of incorporating temporal information in DCPGS and evaluate the resulting temporal-geo-social clusters.

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