The focus of this thesis is to identifyanomalous spatial windows using clustering based methods. Spatial Anomalous windows are the contiguousgroupings of spatial nodes which are unusual with respect to the rest of thedata. Many scan statistics based approaches have been proposed for theidentification of spatial anomalous windows. To identify similarly behavinggroups of points, clustering techniques have been proposed. There are parallelsbetween both types of approaches but these approaches have not been usedinterchangeably. Thus the focus of our work is to bridge this gap and identify anomalous spatial windows usingclustering based methods. Specifically, we use the circular scan statisticbased approach and DBSCAN to bridge the gap between clustering and scanstatistics based approach. Our approach consists of the following steps: (a)Use the parameters proposed by DBSCAN to find core spatial nodes and its neighbors(b) Take combinations of nodes within a neighborhood to find smaller sub-setsof potentially anomalous windows (c) Take unions of all the combinations toexplore bigger sub-sets of potentially anomalous windows. (d) Computetest-statistic for each of the window to identify its degree of unusualness.The window with the highest value of test statistic is the most unusual ascompared to the rest of the data. We present extensive experimental results inUS crime data set for various regions. Our results show that our approach is effectivein identifying spatial anomalous windows and generally performs equal or betterthan existing scan statistic techniques and does better than a pure clusteringmethod
