Analysis of hard clustering algorithms applicable to regionalization

Regionalization is one of the major issues faced by spatial data mining while representing social and economic geography. The purpose of this paper is to develop a system that applies data mining techniques to study air quality distribution of Chennai, a metro city in India using vehicular networking and map the distribution to geographic locations for effective policy making. Three different hybrid clustering methods are analyzed for grouping sites into non-overlapping, contiguous and homogeneous regions. This paper also validates homogeneity of the regions formed and suggests future lines of research for improving these methods.