Summing Up

Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. A crime analyst, using data mining, can extract useful information from very large and complex data sets. In Chapter 10, we further explore practical intelligence applications.

Questions for Discussion

  • 1 What might be some new and unique applications of data mining in police work?
  • 2 What might be some of the challenges of data mining for the intelligence analyst?

Important Terms

Association rules: In data mining, association rules are useful for analyzing and predicting the behavior of individuals.

Clustering: Process of grouping data into a set of meaningful subclasses, called clusters.

Data mining: Process of analyzing data from different perspectives and summarizing them into useful information. Data mining systematically searches information to identify relationships and patterns.

Entity extraction: Data mining approach that identifies particular patterns from data, such as text, images, or audio materials. In criminal justice, it has been used to identify a persons addresses, vehicles, and personal characteristics.

Information technology: Use of any computers, storage, networking, and other physical devices, infrastructure, and processes to create, process, store, secure, and exchange all forms of electronic data.

Machine learning: Type of Al that provides computers with the ability to learn without being explicitly programmed. Machine learning generally focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

Sequential mining: Data mining approach that is concerned with finding statistically relevant patterns within databases.

Transaction processing system: Supports the processing of a company’s or organizations business transactions.

Study Guide Questions

For questions 1—3, indicate whether the statement is true or false.

  • 1 Data mining is essential in criminal justice because trying to determine relationships and patterns within databases is often too complex to figure out in other ways.
  • 2 One strategy for extracting meaning from large amounts of investigative information is the use of video game applications.
  • 3 To find out information about almost any suspect, an intelligence analyst need only “Google” that person s name.

4 To comb through many large databases to find useful information in an investigation, the crime analyst may find similarities through

a Entity extraction

b Clustering

c Associations

d Sequential pattern mining

5 In criminal justice, frequent sequence mining could help determine the

interval between certain types of crimes and the of criminal


a Sequence

b Violence

c Motivation

d Economic variables


Applegate, L.M., Cash, J.I., and Mills, D.Q. (1988). Information technology and tomorrows manager. Harvard Business Review, 66(6): 128—136.

Borhanazad, H. (2014). Artificial neural network, part I: What is a neural network? ResearchGate. Available at: ARTIFICIAL_NEURAL_NETWORK_PART_1

Chen, H., Chung, W., Xu, J., Wang, G., Chau, M., and Qin,Y. (2003). Crime data mining: A general framework and some examples. IEEE Computer, 37(4): 50—56.

Furnas, A. (2012, April 3). Everything you wanted to know about data mining but were afraid to ask. The Atlantic. Retrieved from:

Mahar, F. (2003). Role of information technology in transaction processing system. Information Technology Journal, 2: 128—134.

McCue, C., and Parker, A. (2003). Connecting the dots: Data mining and predictive analytics in law enforcement and intelligence analysis. Police Chief, 70(10): 115-119.

Nath, S.V. (2006). Crime pattern detection using data mining. In International Conference on Web Intelligence and Intelligence Agent Technology Workshops. Hong Kong, pp. 41—14.

Tessier, H. (2015, October—November). The innovative mainframe. Enterprise Tech Journal. Retrieved from: Innovative+Mainframe/2327575/281664/article.html

University of Wisconsin—Madison, (n.d.). A basic introduction to neural networks. Department of Computer Science, University ofWisconsin-Madison. Retrieved from:http://pages.

Wang.T, Rudin, C.,Wagner,D.,and Sevieri.R. (2013). Learning to detect patterns of crime. In H.Blackiel, K. Kersting.S. Nyssen.and EZdezny (eds.), Machine Learning and Knowledge Discovery in Databases. Berlin & Heidelberg: Springer, pp. 515-530. Available at:

< Prev   CONTENTS   Source   Next >