Prajvalarani Sushil Kumar Bogar
Computer Science
June 2025
Crime record bureaus and police stations have begun using data mining techniques in response to the demands placed on them by the large amounts of data held by police departments, as well as the variety and severity of criminal actions. With a reasonable amount of data, an experienced police officer may accurately assess crime patterns; nevertheless, as the number of instances and the severity of crimes rise, the quantity of facts and statistics also increases. Instead of tracking down specific offenders, the emphasis is on criminal locations. The police will be the primary users of the system, and they will occasionally be able to forecast when crimes are likely to occur and the likelihood that they will occur in the near future. This paper primarily examines the kmeans clustering method as it pertains to data mining. It explores how this algorithm might be employed to identify areas prone to criminal activity and to forecast the likelihood of future criminal incidents. To better allocate resources, such as police officers, to areas with a higher crime rate, this study might be useful to the country's law enforcement.
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