Supervised Learning Perspective in Logic Mining

Supervised Learning Perspective in Logic Mining

Blog Article

Creating optimal logic mining is strongly dependent on how the learning data are structured.Without optimal data structure, intelligence systems integrated into logic mining, such as an artificial neural network, tend to converge to suboptimal solution.This paper proposed a novel OCTOPUS: operation control system for task optimization and job parallelization via a user-optimal scheduler logic mining that integrates supervised learning via association analysis to identify the most optimal arrangement with respect to the given logical rule.By utilizing Hopfield neural network as an associative memory to store information of the logical rule, the optimal logical rule from the correlation analysis will be learned and the corresponding optimal induced logical rule can be obtained.

In other words, the optimal logical rule increases the chances for the logic mining to locate the optimal induced logic that generalize the datasets.The proposed work is extensively tested on a variety of benchmark datasets with various performance metrics.Based on the experimental results, the proposed supervised logic mining demonstrated superiority and the least competitiveness compared Evaluating the Provincial Websites of Iran Public Libraries Foundation: A Webometric Study to the existing method.