Authors
Devendra Sharma, Aman Sharma
Abstract
Data mining is the extraction of relevant details, patterns and trends from heavy data. Data mining involves techniques such as clustering, classification, association and regression. There are various applications of data mining that uses various tools that supports different algorithms. This paper provides various data mining techniques and can also applied in the educational sector, marketing, detection of frauds, the telecommunication and manufacturing. As data mining is the notion of all methods and techniques that allows the analysis of large set of data to extract and discover unknown structures and their relations out of large details. Keywords: data mining techniques, Fraud detection, Clustering
Introduction
Data mining extraction and mining of the significant or relevant data from large amount of data and it is also known as “Knowledge mining”. The technology for storing and collection of data has made the accumulation of large amounts of data affordable. This stored data is exploited for making the extraction useful and information should be actionable which is considered as the goal of the generic activity that is referred to as data mining.
This discipline is a field of computer science that basically involves the computational (digital) process of data sets in large quantity and then transform it into a structure that should be understandable for further usage (Jain and Srivastava, 2013).
The Information Development Technology has produced databases in large amount in various areas or regions. The database and information technology research has opened doors to some approaches that stores and manipulates the previously existed data for the decision making process.
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| APA Style | Sharma, D., & Sharma, A. (2019). Data Mining Techniques: A Study. Academic Journal of Computer Sciences, 1(1), 13-17. |
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