We present a new approach for detecting related crime series, by unsupervised learning of the latent feature embeddings from narratives of crime record via the Gaussian-Bernoulli Restricted Boltzmann Machines (RBM). This is a drastically different approach from prior work on crime analysis, which typically considers only time and location and at most category information. After the embedding, r...

Through the boosting accessibility of spatial and temporal data in many research fields, spatial clustering and spatial outlier detection has received a group of concentration in the spatial data mining research. As a very famous method, the CLIQUE Optimization finds a region that deviates significantly from the entire spatial data set. In this paper, we introduce the novel problem of mining cr...

There is an abnormal increase in the crime rate and also the number of criminals is increasing, this leads towards a great concern about the security issues. Crime preventions and criminal identification are the primary issues before the police personnel, since property and lives protection are the basic concerns of the police but to combat the crime, the availability of police personnel is lim...

Intention recognition has significant applications in ambient intelligence, assisted living and care of the elderly, games and intrusion and other crime detection. In this chapter we explore an approach to intention recognition based on clustering. To this end we show how to map the intention recognition problem into a clustering problem. We then use three different clustering algorithms, Fuzzy...

A significant amount of academic research in criminology focuses on spatial and temporal event analysis. Although several efforts have integrated spatial and temporal analyses, most previous work focuses on the space-time interaction and space-time clustering of criminal events. This research expands previous work in geostatistics and disease clustering by using a Bayesian hierarchical framewor...

Detecting crime from data analysis can be difficult because daily activities of criminal generate large amounts of data. The police records exist in various formats and the quality of analysis greatly depends on the background knowledge of the analyst. This paper proposes a simple correlation clustering algorithm which aims at finding illegal activities of professional identity fraudsters based...

Crimes will somehow influence organizations and institutions when occurred frequently in a society. Thus, it seems necessary to study reasons, factors and relations between occurrence of different crimes and finding the most appropriate ways to control and avoid more crimes. The main objective of this paper is to classify clustered crimes based on occurrence frequency during different years. Da...

While the presence of clustering in crime and security event data is well established, the mechanism(s) by which clustering arises is not fully understood. Both contagion models and history independent correlation models are applied, but not simultaneously. In an attempt to disentangle contagion from other types of correlation, we consider a Hawkes process with background rate driven by a log G...

Crowd-based events, such as football matches, are considered generators of crime. Criminological research on the influence of football matches has consistently uncovered differences in spatial crime patterns, particularly in the areas around stadia. At the same time, social media data mining research on football matches shows a high volume of data created during football events. This study seek...