Comparative Analysis of Various Spatio-Temporal Data Clustering Techniques in SpatioTemporal Data Mining

Abstract

The data in digital world are increasing exponentially day by day. Spatio-temporal data hold data about an object in space over a period of time. Vast amount of spatiotemporal data is generated on daily basis in different application fields like weather forecasting, traffic flow, geo-tagging in social media etc. Spatiotemporal data mining (STDM) refers to the process of mining interesting and potentially useful patterns from a large spatiotemporal dataset. Clustering is one of the fundamental and important step in data mining process. The clustering is slightly different from trivial clustering technique in data mining. This paper emphasizes the temporal and spatial dimensions of data along with the techniques of spatiotemporal clustering techniques used and the comparative analysis of spatiotemporal algorithms with respect to methodology and complexity.