A multi‐dimensional crime spatial pattern analysis and prediction model based on classification

On Model-Based Clustering, Classification, and Discriminant Analysis

The use of mixture models for clustering and classification has burgeoned into an important subfield of multivariate analysis. These approaches have been around for a half-century or so, with significant activity in the area over the past decade. The primary focus of this paper is to review work in model-based clustering, classification, and discriminant analysis, with particular attenti...

Crime pattern analysis: A spatial frequent pattern mining approach

Crime pattern analysis (CPA) is the process of analytical reasoning facilitated by an understanding about the nature of an underlying spatial framework that generates crime. For example, law enforcement agencies may seek to identify regions of sudden increase in crime activity, namely, crime outbreaks. Many analytical tools facilitate this reasoning process by providing support for techniques s...

mortality forecasting based on lee-carter model

over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...

Multidimensional Texture Analysis for Unsupervised Pattern Classification

Algorithmic Crime Prediction Model Based on the Analysis of Crime Clusters

s Crime is a behavior disorder that is an integrated result of social, economical and environmental factors. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. In this paper we look at use of missing value and clustering algorithm for crime data using data mining. We will look at MV algorithm and Apr...