Text Mining Approach for Discovering Useful Knowledge from Information Sources of E-Waste

Abstract

Proposed method introduces a K-Nearest Neighbor method by using relevance vector machine which finds the entities and related information on waste materials to make processing of waste materials more domain friendly. A corpus analysis was incorporated to support the extraction of accurate information through elimination of unrelated tokens. The distribution of weights to terms was determined through a vector space model. Parallel verification on various entities was carried out while testing. This reduces the time taken for mapping and discovering useful information from documents (dataset) of e-waste management. Recent computer aided tools cannot check for consistency and correctness of faulty requirement definition, this paper introduces text processing method using natural language technique; this enables effective maintenance and utilization of waste materials by presenting task specific information through computer-assisted text mining and analysis process.