Hardik Patel
Reflection 3
Data mining is the process of analyzing data from dierent viewpoints and summarizing it into
some useful information which can be used to increase the revenue or to cut the costs. Data mining is
sometimes also known as Knowledge Discovery concept.
Roots of data mining are mainly on statistics, arti cial intelligence, and machine learning
techniques. Data mining is built mainly on statistics, e.g, standard distribution, regression analysis,
standard variance], standard deviation, cluster analysis etc. which helps to study data and data
relationships. Arti cial intelligence is built upon heuristics which a$empts to apply the processing in
terms of human thoughts to statistical trends. Machine learning is the combination of both statistics and
Arti cial Intelligence. It is an evolution of arti cial intelligence, because it mergers heuristics with
advanced statistical analysis. Machine learning provides the self-learning ability about the data they
study, so that same programs can make dierent decisions based on the qualities and quantities of the
analyzed data, using statistics for fundamental concepts, and Advanced Arti cial Intelligence heuristics
and algorithms to achieve its goals.
Data mining is fundamentally the variation of machine learning techniques for business
applications. Data mining can also be described as the union of historical and recent developments in
statistics, Arti cial Intelligence, and machine learning. These methods are used together to study and
analyze the data and nd hidden trends or pattern. It is the process of nding correlations or pa$erns
among various elds in large relational databases. Data mining provides the link between OLTP and
