A Comparison of ABK-Means Algorithm with Traditional Algorithms IJTSRD
Crime investigation has very difficult task for police.Department of police plays an important role for identifying the criminals and their related information. It is observable that there are so manyamounts of increases in the crime rate due to the gap between the limitedusagesof investigation technologies. So, there are various new opportunities for the developing a new methodologies and techniques in this field for crime investigation. Using the methods like image processing, based on data mining, forensic, and social mining. Developing a good crime analysis tool to identify crime patterns quickly and efficiently for future crime pattern detection is required. Data mining is a process that uses a variety of data analysis tools to discover patterns and relationships in data that may be used to make valid predictions. Data mining techniques are the result of a long process of research and product development. Data mining is the computer-assisted process to break up through and analyzing large amount of data. Then extracting the meaningfuldata. The proposed terminology provides combine approach of preprocessing by NLP clustering, outlier detection and rule engine to identify the criminals. To automatically group the retrieved data into a list of meaningful categories different clustering techniques can be used here we used the new approach to clustering i.e combination of K-medoid and Bisecting K-means algorithm for clustering. Crime area somewhat helps to find out the criminals so in this work we focus on area wise analysis with require records. Those records having all information about criminals which helps to further investigation. In this paper we compare ABK-means algorithm with three basic clustering algorithms i.e. K-means K-medoid, and Bisecting K-means on crime Denver dataset on the basis of time and accuracy.
by Ms. H. N. Gangavane”A Comparison of ABK-Means Algorithm with Traditional Algorithms”
Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017,
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