Crime Case Reasoning Based Knowledge Discovery Using Sentence Case Relative Clustering for Crime Analyses

Abstract

Day to day involvement in crime becomes higher statistics for providing information against crime occurrences. A crime committed in different locations, the point of crime occurrence, strategy be analyzed very tedious using only information records. Because information collection in the form of attribute case records with direct crime rates score, so valid factor identification of crime category is a problem. By using the crime cluster in data mining technique to analyze the criminal records to propose a sentence case relative clustering algorithm (SCRCA)with addition classification rule mining algorithm to solve the crime problems. Also to use the sentence case observer technique for knowledge discovery from the crime records for proper case identification from sentence case records and to help increase the predictive accuracy. Crime examines a developing method and identifying the field in law implementation without standard definitions for correct judgment. With the expanding utilization of the clustering automated frameworks to track crimes, information examiners helping the law implementation officers and analysts to accelerate the way toward measuring crimes. The main contribution is to analyses the attribute case with relative sentences of count word analyzes factor for improving the crime prediction for categorizing crime type.