Integration of K_Means and Decision Tree for Knowledge Extraction from a Database

Main Article Content

D.L. Elshowakh
A. E. ELAlfy, A. F. ELGamal


Data mining is the process of discovering previously unknown and potentially interesting patterns in databases. Though most
knowledge discovery methods have been developed for supervised data, the task of finding knowledge from unsupervised data often arises in
real-world problems. In addition, techniques for unsupervised knowledge discovery are essentially different and still much less developed than
those for supervised discovery. This paper introduces a novel framework for extracting a set of comprehensible rules from unsupervised
database. The proposed framework depends on three techniques namely; clustering technique, fuzzification technique, and inductive learning
technique. Clustering technique uses a k-means for clustering unsupervised database. Consequently the input database is converted into
supervised database. Fuzzification technique transforms the continuous attributes of database into linguistic terms. This transformation leads to
reduction of search space. Decision tree used as a inductive learning algorithm for extracting a set of accurate rules from supervised database.


Keywords: Unsupervised Database; K_Means; Decision Tree; Clustering Technique; Rule Extraction.


Download data is not yet available.

Article Details