HISTORY OF DATA MINING
The term "Data Mining" emerged in the 1990s, but its roots extend far deeper, tracing back through a rich history of pattern recognition in data.
Early techniques for identifying patterns in data include Bayes' theorem from the 1700s and the development of regression analysis in the 1800s. With the advent of computer science, the capacity to collect, store, and manipulate data grew exponentially, leading to more complex datasets. The field of data investigation evolved from manual methods to more advanced, automated processes. Key advancements include neural networks, clustering, genetic algorithms (1950s), decision trees (1960s), and support vector machines (1990s).
Data mining's origins are deeply intertwined with three primary domains: Classical Statistics, Artificial Intelligence, and Machine Learning.
Classical Statistics:
Statistics form the foundation of much of the technology underpinning data mining. Key statistical methods such as regression analysis, standard deviation, standard distribution, standard variance, discriminant analysis, cluster analysis, and confidence intervals are essential for analyzing data and understanding data relationships. These techniques are crucial for interpreting and connecting data insights effectively.
Artificial Intelligence:
Artificial Intelligence (AI) relies on heuristics rather than traditional statistics. It aims to mimic human-like thinking to tackle statistical problems. One notable application of AI is in high-end commercial products, such as query optimization modules for Relational Database Management Systems (RDBMS).
