Empirical Evaluation of Grid-Based Text Mining Tasks

Abstract

The enormous amount of information stored in unstructured texts cannot simply be used for further processing by computers, which typically handle text as simple sequences of character strings. Text mining is the process of extracting interesting information and knowledge from unstructured text. This study implies an overview of our research activities aimed at efficient use of Grid infrastructure to solve various text mining tasks. Grid-enabling of various text mining tasks was mainly driven by increasing volume of processed data. Integration of text mining services into the distributed service oriented system enables plenty of various possibilities for building the distributed text mining services. Three different data driven distributed approaches for text mining have been proposed they are induction of decision trees, GHSOM clustering algorithm and FCA method. The objective of this work was to evaluate the concept that proposed by Min Sarnovskartý, which yielded favorable results.