An Approach to Perform Uncertainity Analysis on a Spatial Dataset using Clustering and Distance based Outlier Detection Technique

Background: In past years, many methods have been implemented for maintaining and supervising uncertain data that may occur due to collection of data in new ways which results in missing values, erroneous data. The main aim of this work is to help the end user to get correct information about spatial data. Method: The behaviour of data as an outlier is the result of uncertainty. The challenge in spatial data sets is to cluster uncertain objects. Hence, unsupervised clustering can be used to deal with this type of data. In this paper, the difficulty of outlier detection with uncertain data is examined. Finding: To improve the performance and quality, Voronoi Diagram is used which partition the objects into each cell and helps to see the exact location of an object. The integral part is the pre-processing step of removing uncertainty to avoid wrong interpretation. Furthermore, CLARA (Clustering LARge Applications) algorithm is applied to produce the high quality clusters. It has an in-built function of outlier detection too and it is suitable for large data set. This algorithm uses Mahalanobis Distance to calculate the distance between cluster and its members, to remove outliers and reduce uncertainty for feasible and supporting inputs. This procedure can be a valid provision to be use in geo-database creation. Improvement: The methodology can be enhanced by designing the procedure to develop a Decision Support System (DSS) for spatial database creation.