The Importance Of Crime Investigation

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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 is a so many amount of increases in the crime rate due to the gap between the limited usages of 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…show more content…
In this the data space is partitioned into groups of entities with similar content. So our works focus on clustering on crime dataset. Here we are using ABK-Mean for clustering and clustered the selected dataset in a most efficient manner as well as combine approach of outlier detection and rule engine. In this paper, document clustering for criminal identification is implemented. For clustering of the input document k-means clustering technique is used. In this paper also use the data mining technique Association Rule Mining to improve the search…show more content…
The researchers have identified various limitations of these methods particularly in the areas of information integration systems, text-mining and bio-informatics[19] Karanjit Singh and Dr. Shuchita Upadhyaya (2012) have worked on "Outlier Detection: Applications And Techniques"[31]. The researches ware worked in this paper on to attempt and bring together various outlier detection techniques, in a structured and generic description with some exercise. The research on outlier analysis for their selves as well as for beginners in this research field who could then pick up the links to different areas of applications in details. Jingke Xi (2008) ware worked on "Outlier Detection Algorithms in Data Mining"[20].this paper the author was discusses and compares approach of different outlier detection from data mining perspective, which can be categorized into two categories: classic outlier approach and spatial outlier approach. The classic outlier approach analyzes outlier based on transaction dataset, which can be grouped into statistical-based approach, distance-based approach, deviation-based approach, density-based approach. The spatial outlier approach analyzes outlier based on spatial dataset that non-spatial and spatial data are significantly different from

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