This usually involves using database techniques such as spatial indices.
These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics.
Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s).
The proliferation, ubiquity and increasing power of computer technology has dramatically increased data collection, storage, and manipulation ability.
Lovell indicates that the practice "masquerades under a variety of aliases, ranging from "experimentation" (positive) to "fishing" or "snooping" (negative).
The term data mining appeared around 1990 in the database community, generally with positive connotations.
In the 1960s, statisticians and economists used terms like data fishing or data dredging to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis.
The term "data mining" was used in a similarly critical way by economist Michael Lovell in an article published in the Review of Economic Studies 1983.
Later he started the SIGKDDD Newsletter SIGKDD Explorations.
The KDD International conference became the primary highest quality conference in data mining with an acceptance rate of research paper submissions below 18%.