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Sathyaraj, S. R.
- Area Based Crime Analysis in Spatial Data Mining Approach for Association Rule in Geo-Referenced Data
Abstract Views :195 |
PDF Views:2
Authors
Affiliations
1 DRDO-BU CLS, Bharathiar University, Coimbatore-641046, IN
2 Sri Ramakrishna Mission Vidyalaya, Coimbatore-641020, IN
3 CJSS University, Mysore-500015, IN
1 DRDO-BU CLS, Bharathiar University, Coimbatore-641046, IN
2 Sri Ramakrishna Mission Vidyalaya, Coimbatore-641020, IN
3 CJSS University, Mysore-500015, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 2 (2012), Pagination: 59-63Abstract
In this paper which passion to integrate a large volume of data sets into useful information by adopting a various information techniques in the most modern technology world. The adopted approaches of Single variate Association Rule for Area to Crime based on the knowledge discovery techniques such as, clustering and association-rule mining. It reveals with an inherent of patterns of information into a fruitful exploratory tool for the discovery of spatio-temporal patterns. This tool is an autonomous pattern detector to reveal plausible cause-effect associations between layers of point and area data. We present VATA algorithm with an exploratory analysis for the effectively explore geo-referenced data. The present study of this paper was focuses through the real crime dataset by using algorithm. We demonstrate approach to a new type of analysis of the spatio-temporal dimensions of records of criminal events. We hope this will lead to new approaches in the exploration of large volumes of spatio-temporal data.Keywords
Algorithm, Clustering Association Rule, Crime Data, Data Mining, GIS, Spatio-Temporal Data.
- Association Rule-Spatial Data Mining Approach for Geo-Referenced in Crime to Crime Analysis
Abstract Views :210 |
PDF Views:2
Authors
Affiliations
1 DRDO-BU CLS, Bharathiar University, Coimbatore-641046, IN
2 Department of MCA, Sri Ramakrishna Mission Vidyalaya, Coimbatore-641020, IN
3 Department of Environmental Science, JSS University, Mysore-500015, IN
1 DRDO-BU CLS, Bharathiar University, Coimbatore-641046, IN
2 Department of MCA, Sri Ramakrishna Mission Vidyalaya, Coimbatore-641020, IN
3 Department of Environmental Science, JSS University, Mysore-500015, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 1 (2012), Pagination: 31-36Abstract
Spatial data mining is a demanding field since huge amounts of spatial data that has been processed and turned into useful information by this paper. The increased crime rate and enormous amount of data being stored in crime databases by police personnel which has been collected from various jurisdiction of Coimbatore are gathered for the application of technologies which provides the means to turn data into information by data fusion and data mining. Data fusion organizes, combines and interprets information from multiple sources and it overcomes confusion from conflicting reports and cluttered or noisy backgrounds. Data mining is concerned with the automatic discovery of patterns and relationships with (crime to crime) in large databases. Technically, it is the process of finding correlations or patterns among dozens of fields in large relational databases using the tools of GIS. This paper provides a clear finding to prevent from crime with associated to another crime occurrence with the naked observation on correlation between one crime to another crime.Keywords
Algorithm, Association Rule, Data Mining, Crime Data, GIS.- Analysis and Pattern Deduction on Linguistic Based Mean and Fuzzy Association Rule Algorithm on any Geo-Referenced Crime Point Data
Abstract Views :231 |
PDF Views:2
There are 3 algorithms to study the pattern of any point data and for better inferences and interpretation. In this study, Mean Algorithm using Linguistic variable finds the most occurred crime at particular location among different types of crime. Fuzzy associations rule algorithm on point data formulate the rules among the crimes is a novel means for knowledge discovery in the crime domain, supported by experimental results using Mapobject and VB. Mean algorithm using crime find the location not shown by earlier algorithm where sensitivity of crime is high.
Authors
Affiliations
1 Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore, IN
2 DRDO-BU CLS, Coimbatore, IN
3 JSS University, Mysore, IN
1 Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore, IN
2 DRDO-BU CLS, Coimbatore, IN
3 JSS University, Mysore, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 14 (2011), Pagination: 846-849Abstract
Data mining is receiving more attention to find the underlying patterns in crime data. It is need to act quickly to reduce crime activity and find out the links between various available data sources. The government is continuing to call upon modern geographic information systems to find the more intensive area of crime in order to protect their communities and assets. Real time solutions can provide significant resources and push the capability of law enforcement closer to the pulse of criminal activity.There are 3 algorithms to study the pattern of any point data and for better inferences and interpretation. In this study, Mean Algorithm using Linguistic variable finds the most occurred crime at particular location among different types of crime. Fuzzy associations rule algorithm on point data formulate the rules among the crimes is a novel means for knowledge discovery in the crime domain, supported by experimental results using Mapobject and VB. Mean algorithm using crime find the location not shown by earlier algorithm where sensitivity of crime is high.