What is Data Mining?

Data Mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. It is an interdisciplinary subfield of computer science. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it 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 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, machine learning, and business intelligence. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining).

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Machine Learning Definition

Machine learning is subfield of science, that provides computers with the ability to learn without being explicitly programmed.   The goal of machine learning is to develop learning algorithms, that do the learning automatically without human intervention or assistance, just by being exposed to new Data Science The machine learning paradigm can be viewed as “programming by example”. This subarea of artificial intelligence intersects broadly with other fields like, statistics, mathematics, physics, theoretical computer science and more.