Application of Markov Process Model and Entropy Analysis in Data Classification and Information Retrieval
Journal Title: International Journal on Computer Science and Engineering - Year 2010, Vol 2, Issue 3
Abstract
This paper proposes a statistical approach by a modified arkov chain process model and entropy function in the analysis of a arge data set. The basic idea is that entropy nd conditional ntropy are used to measure the information ntent. In such analysis of large data sets including signal and image processing, unsupervised partitioning of data is required to uild similar classes or clusters. The idea behind this is to dentify ach data item unambiguously as a member of articular class or cluster. The issue of partitioning is viewed as an information theoretic problem and it has been shown that he minimization of partitioning entropy may be used to aluate the most probable set of data items. The data set onsidered for the simulation are the scanned OMR pplication forms of the candidates applying in various courses of a iversity. Classes are defined and inter dependence is easured on the basis of Markov process odel and entropy nalysis.
Authors and Affiliations
Udayan Ghose , C. S. Rai , Yogesh Singh
Keywords
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