Data mining tools are software components and theories that allow users to extract information from data. The tools provide individuals and companies with the ability to gather large amounts of data and use it to make determinations about a particular user or groups of users. Some of the most common uses of data mining tools are in the fields of marketing, fraud protections and surveillance.
The manual extraction of data has existed for hundreds of years. However, the automation of data mining has been most prevalent since the dawn of the computer age. During the 20th century, various computer sciences emerged to help support the concept of developing data mining tools. The overall goal of the utilization of the tools is to uncover hidden patterns. For example, if a marketing company finds that a person takes a monthly trip from New York City to Los Angeles, it becomes beneficial for that company to advertise details of the destination to the individual.
Within the data mining industry, standards have been established to define the parameters of the use of data mining tools. Annually, the Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) holds a meeting to determine what processes are used. The same group is also responsible for assessing the ethical implications of the analysis of data from individuals and companies. A biannual journal is published by the group entitled SIGKDD Explorations.
The most prevalent tool used in data mining is the process called Knowledge Discovery in Databases (KDD). KDD was developed in 1989 by Gregory Piatetsky-Shapiro. Using this data mining tool, users are able to process raw data, mine the data for information and interpret the various results in the form of information management.
One of the most important forms of data mining tools is used for combating terrorism in the 21st century. In the United States, the National Research Council uses the concepts of pattern mining and subject-based data mining to identify terrorist activity in the large pool of information around the world. Pattern mining is defined by the process of locating patterns within a large volume of data. Subject-based data mining attempts to identify relationships between individuals. Both techniques can also be utilized in general business practice by defining the mindset of a customer base and the interactive relationship between customers.