Efficient Crime Analysis Based on Hybrid Approach by Combining Dynamic Time Wrapping Algorithm with K-Means Clustering Approach.

Abstract

Abstract: Crime is a preeminent issue where the main concern has been worried by individual, the local area and government. Wrongdoing forecast utilizes past information and in the wake of investigating information, anticipate the future wrongdoing with area and time. In present days sequential criminal cases quickly happen so it is a provoking assignment to anticipate future wrongdoing precisely with better execution. Clustering different time series into similar groups is a challenging clustering task because each data point is an ordered sequence. The most common approach to time series clustering is to flatten the time series into a table, with a column for each time index (or aggregation of the series) and directly apply standard clustering algorithms like k-means. But this doesn’t always work well on Time Series Data. The paper focuses on combining the features of K-Means Clustering algorithm with Dynamic Time Wrapping Algorithm for efficient Crime prediction and analysis