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A Unified Theoretical Framework for Data Mining
Author(s) -
Dost Muhammad Khan,
NAWAZ MOHAMUDALLY,
D.K.R. Babajee
Publication year - 2013
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2013.05.015
Subject(s) - computer science , knowledge extraction , variety (cybernetics) , data mining , data science , process (computing) , information extraction , outcome (game theory) , information retrieval , artificial intelligence , mathematics , mathematical economics , operating system
The pattern extraction and discovery of useful information from a dataset are the foremost purposes of data mining; the outcome of this process is the ‘knowledge’ which is helpful in taking the decision. For the past decade there have been multiple attempts and strong beliefs in the development and the formulation of the unified data mining frameworks that would answer to the fundamental versions related to the discovery of knowledge. In this paper we are presenting a novel unified framework for data mining conceptualized through the composite functions. The framework is further illustrated with a variety of real life datasets using different data mining algorithms

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