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History of Data Mining

In the 1990s, the term "Data Mining" was introduced, but data mining is the evolution of a sector with an extensive history.

Early techniques of identifying patterns in data include Bayes theorem (1700s), and the evolution of regression(1800s). The generation and growing power of computer science have boosted data collection, storage, and manipulation as data sets have broad in size and complexity level. Explicit hands-on data investigation has progressively been improved with indirect, automatic data processing, and other computer science discoveries such as neural networks, clustering, genetic algorithms (1950s), decision trees(1960s), and supporting vector machines (1990s).

Data mining origins are traced back to three family lines: Classical statistics, Artificial intelligence, and Machine learning.

Classical statistics:

Statistics are the basis of most technology on which data mining is built, such as regression analysis, standard deviation, standard distribution, standard variance, discriminatory analysis, cluster analysis, and confidence intervals. All of these are used to analyze data and data connection.

Artificial Intelligence:

AI or Artificial intelligence is based on heuristics as opposed to statistics. It tries to apply human- thought like processing to statistical problems. A specific AI concept was adopted by some high-end commercial products, such as query optimization modules for Relational Database Management System(RDBMS).

Machine Learning:

Machine learning is a combination of statistics and AI. It might be considered as an evolution of AI because it mixes AI heuristics with complex statistical analysis. Machine learning tries to enable computer programs to know about the data they are studying so that programs make a distinct decision based on the characteristics of the data examined. It uses statistics for basic concepts and adding more AI heuristics and algorithms to accomplish its target.

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