Clusters

A cluster is a subset of objects that are similar. Clustering is the process of grouping data into a set of meaningful subclasses, called clusters. For example, in the insurance industry, you may want to group together certain policy holders, say, for instance, all policy holders with high average claim requests and payouts. By identifying this cluster, you can decide how to target that group to reduce its claims.

In criminal justice, crime analysts have started helping detectives and other law enforcement officers to speed up the process of solving crimes. More specifically, a data mining approach using clustering-based models can help in the identification of crime patterns. But providing that help has not been easy because the data related to crime and criminals are often scattered in various databases and around the Internet. Some data are kept confidential, while other data are public information. Data about county prisoners are usually found in the county or the sheriff’s sites. However, data about crimes related to narcotics or juvenile cases are often more restricted. Similarly, information about sex offenders is made public to warn others in the area, but the identity of the victim is often not accessible. Thus, as a data miner, the analyst has to deal with various issues—and databases—to mine crucial data for detectives.

Furthermore, sheriffs’ offices and police departments may use a computerized reporting system, or they may still use the traditional paper-based crime reports. Whether these crime reports are computerized or paper, they almost always contain certain basic information: the type of crime, the date and time of the crime, the location of the crime, the names and addresses of the victims, the names and addresses of the witnesses, and the name and address of the suspect. Additionally, there is the narrative or description of the crime and MO, both of which are usually in the form of text. That is, police officers and detectives use free text to record certain facts, observations, and conclusions. This is information that cannot be included by checking boxes on a police department form. While some information can be stored in computer databases as numeric, character, or date fields of tables, the observations and conclusions are often stored as free text.

And therein lies the challenge in data mining crime data. Combing through hundreds or thousands (or even more) of crime reports to locate data (such as a descriptions of crime perpetrators or the names of suspects) to gather them into data mining categories is not always an easy job. And that’s where clustering in data mining comes in. A cluster is a group of crimes or people or other kinds of data that are similar and may represent a geographical region, a hot spot of crime, or a possible crime pattern.

Clustering algorithms in data mining are equivalent to the task of identifying groups of records that are similar between themselves but different from the rest of the data. In some instances, clusters will be useful for identifying a crime spree committed by one person or a group of suspects. Given this information, the next challenge is to find the variables providing the best clustering. These clusters will then be presented to the detectives to “drill down” (meaning move to another, often lower or more basic, level of analysis) using their expertise as detectives.

However, clustering requires a skilled crime analyst who is aware that data mining is sensitive to the quality of input data. What that means is that law enforcement officers’ reports may be inaccurate or have missing information, or that the data entry step was flawed because names or locations were misspelled, for instance. The skilled and experienced data miner must have a good knowledge of clustering, and know what software will perform the tasks that he or she requires, all the while working closely with a detective, at least in the initial phases of the investigation (Nath, 2006).