A Comparative Study to analyze crime threats using data mining and machine learning approach

Abstract

In the last decade, with the development of new technologies crime rate is increasing rapidly. Nowadays, the crime rate analysis and identification is one of the systematic approaches to reduce the illegal activities in all around the world. Thus, this is the biggest challenge to the IT field to solve these critical issues by applying some tools and technologies. Data mining along with the machine learning approach is one of the efficient as well as best approaches to solve this critical issue. It provides the method to identify region and criminal in an appropriate time. In paper is basically emphasis on various machine learning algorithms such as K-means, SVM, A priori Algorithm, CART algorithms, Fuzzy-C and FP tree and machine learning is used to learn the pattern and matches it with actual data. This process is used to reduce the normalized the data and to delete all anomalies and provide better result.