Discretization Technique Using Maximum Frequent Values and Entropy Criterion

Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Topic: Data Management
Format: PDF
Discretization is a process of dividing a continuous attribute into a finite set of intervals to generate an attribute with small number of distinct values. Discretization not only produce a concise summarization of continuous attributes to help the experts understand the data more easily, but also make learning more accurate and faster. The existing system, EDISC (Entropy-based Discretization Intervals using Scope of Classes) considers the scope of each class, and then calls the standard Ent-MDLP procedure for the scope of each class.

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