A GIS anchored system for clustering discrete data points – A connected graph based approach

Abstract

Clustering is considered as one of the most important unsupervised learning problem which groups a set of data objects, in such way, so that the data objects belongs to the same group (known as cluster) are very similar to each other, compared to the data objects in another group (i.e. clusters). There is a wide variety of real world application area of clustering. In data mining, it identifies groups of related records, serving as the basis for exploring more detailed relationships. In text mining it is heavily used for categorization of texts. In marketing management, it helps to group customers of similar behaviors. The technique of clustering is also heavily being used in GIS. In case of city-planning, it helps to identify the group of vacant lands or houses or other resources, based on their type, value, location etc. To identify dangerous zones based on earth-quake epi-centers, clustering helps a lot. In this paper, a set of data objects are clustered using two connected graph based techniques – MST based clustering and Tree Based clustering. After considering a lot of test cases, at the end of the paper, the second technique is found to be more suitable for clustering than the first one.