Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of
computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data
management aspects, data pre-processing,
model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term "data mining" is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including
artificial intelligence (e.g., machine learning) and
business intelligence. The book Data mining: Practical
machine learning tools and techniques with Java (which covers mostly
machine learning material) was originally to be named just Practical machine learning, and the term
data mining was only added for
marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods,
artificial intelligence and
machine learning – are more appropriate.
Relevant Topics in General Science
