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International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 4 Issue 4, June 2020 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD31368 | Volume – 4 | Issue – 4 | May-June 2020 Page 1333
Overview of Data Mining
Department of Computer Science Engineering, Dronacharya College of Engineering, Gurugram, Haryana, India
Data mining is the 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 sub field of computer science
and statistics with an overall goal to extract from a data set and transform the
information into a comprehensible structure for further use. The
process of digging through data to discover hidden connections and predict
future trends has a long history. Sometimes referred to as ‘knowledge
discovery’ in databases, the term data mining wasn’t coined until the 1990s.
What was old is new again, as data mining technology keeps evolving to keep
pace with the limitless potential of big data and affordable computing power.
Over the last decade, advances in processing power and speed have enabled us
to move beyond manual, tedious and time-consuming practices to quick, easy
and automated data analysis. The more complex the data sets collected, the
more potential there is to uncover relevant insights.
KEYWORDS: database, data mining, techniques
How to cite this paper: Rupashi Koul
“Overview of Data Mining” Published in
of Trend in Scientific
(ijtsrd), ISSN: 2456-
6470, Volume-4 |
Issue-4, June 2020,
Copyright © 2020 by author(s) and
International Journal of Trend in Scientific
Research and Development Journal. This
is an Open Access article distributed
under the terms of
License (CC BY 4.0)
The manual extraction of patterns from data has occurred
for centuries. Early methods of identifying patterns in data
include Bayes’ theorem (1700s) and regression analysis
(1800s). The proliferation, ubiquity and increasing power of
computer technology have dramatically increased data
collection, storage, and manipulation ability. As data sets
have grown in size and complexity, direct data analysis has
increasingly been augmented with indirect, automated data
processing, aided by other discoveries in computer science,
specially in the field of machine learning, such as neural
networks, cluster analysis, genetic algorithms (1950s),
decision trees and decision rules (1960s), and support
vector machines (1990s). Data mining is the process of
applying these methods with the intentio
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