Mining interestingness patterns on Lean Six Sigma for process and product optimisation

Abstract

The paper seeks to find from textual online data the frequent terms in news media on Lean Six Sigma (LSS), the areas and mode of application. The paper also purposes to identify the association between the eight kinds of waste in LSS against the frequent terms. This paper uses the web mining and text mining techniques of data mining to extract 1203 textual data from Google news for analysis. The R programming language web mining plugin is used for the web text extraction and analysis for frequent terms, correlation and association. The research identifies the key terms in LSS mainly used by companies, academia, researchers and other users of news media. Seven of the eight major kinds of waste in LSS were frequent in news media on google news. The paper also reveals the manner of online information contained in online featured on the internet on LSS. The paper assists online information seekers, industry players and policy formulators in tuning the concept along the goal for online news formulation and industrial adoption. The paper uses textual data from Google search engine, transforms and analyses the data by the use of the R data mining tool.