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Thursday, 28 April 2016

How does Data Mining help in Predictive Analysis?

Data mining is defined as shifting through very large amounts of data for useful information. Some of the most important and popular data mining techniques are association rules, classification, clustering, prediction and sequential patterns. Data mining techniques are used for a  variety of applications.

Data Mining is the process of extracting hidden knowledge from large volumes of raw data. The knowledge must be new, not obvious, and one must be able to use it. Data mining has been defined as the nontrivial extraction of previously unknown, implicit and potentially useful information from data. It is “the science of extracting useful information from large databases”. It is one of the tasks in the process of knowledge discovery from the database.

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). These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. In the health-care industry, data mining plays an important role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data mining technique the number of tests should be reduced. This reduced test plays an important role in time and performance.

Data Mining is used to discover knowledge out of data and presenting it in a form that is easily understand to humans. It is a process to examine large amounts of data routinely collected. Data mining is most useful in an exploratory analysis because of nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers.



Article by Rishibha Tuteja
Last minute Blogger, fangirl by profession. A Bibliophile by heart, Tech–Enthusiast by choice.
She breathes dreams like air and can be reached at  https://twitter.com/BibliophileRish
 
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