University of Twente Student Theses


Spatial Clustering with Web Processing Services (WPS)

Konga, Hemanth (2013) Spatial Clustering with Web Processing Services (WPS).

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Abstract:This research would facilitate the application of Geospatial Clustering Algorithms on spatial data as Web Processing Service. The crime-analysis section studies daily reports of serious crimes in order to determine the different facts of the crime like location, time, special characteristics, similarities to other criminal attacks, and various significant facts that might help to identify either a criminal or the existence of a pattern of criminal activity. Data mining deals with the discovery of unexpected patterns and new rules that are “hidden” in large databases. Data mining is the process of extracting knowledge from these large databases. It serves as an automated tool that uses multiple advanced computational techniques, including artificial intelligence (the use of computers to perform logical functions), to fully explore and characterize large data sets involving one or more data sources, identifying significant, recognizable patterns, trends, and relationships not easily detected through traditional analytical techniques alone. Data mining can be defined as the identification of interesting structure in data, where structure designates patterns, statistical or predictive models of the data, and relationships among parts of the data. Spatial data mining is the process of extracting interesting knowledge from spatial databases. The spatial databases contain objects that represent space. The spatial data represents topological and distance information. This spatial object is organized by spatial indexing structures. Spatial data mining, or knowledge discovery in spatial database, refers to the extraction of implicit knowledge, spatial relocations, or other patterns not explicitly stored in spatial databases. Clustering is an essential task in data mining to group data into meaningful subsets to retrieve information from a given dataset of Spatial Data Base Management System (SDBMS). The information thus retrieved from the SDBMS helps to detect urban activity centers for consumer applications. Clustering algorithms group the data objects into clusters wherein the objects within a cluster are more similar to each other and are more dissimilar to objects in other clusters. Spatial clustering is a major component of spatial data mining and is implemented as such to retrieve a pattern from the data objects distribution in a given data set. The clusters thus obtained would have to be interpreted to determine each one’s significance in the context for which the clustering is carried out. Spatial clustering by itself is quite significant in that it is being implemented in a wide range of applications. Clustering process is a significant step towards the decision making process in such application areas as public safety measures, consumer related applications, ecological problems, public health measures and effective transportation facilities. Keywords: Web Processing Services (WPS), Crime Analysis, Geo Spatial Clustering, Clustering Algorithms, K-Means, DB Scan.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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