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?
Association rules: In data mining, association rules are useful for analyzing and predicting the behavior of individuals.
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.
Clustering: Process of grouping data into a set of meaningful subclasses, called clusters.
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 person’s 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 AI 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 organization’s business transactions.