Text mining, clustering, and forecasting horizons ahead in the field of quality and productivity

Abstract

Investigating research trends in a scientific field during different time periods can provide a better understanding for researchers. They can properly plan for future research and allocation of necessary resources. The primary purpose of this study is to focus on the field of quality and productivity (QaP). In this regard, the data mining technique is used for clustering. Moreover, the general trend of each research is discussed in this study from different aspects. To extract the data, the Scopus database has been used as a complete database of scientific articles. Clustering is applied to the abstracts of 17,302 valid articles during the last 20 years (2000-2019). After using various techniques to prepare textual data, clusters are created using the k-means technique that can assign thematic labels in the field of QaP. Box-Jenkins approach is applied to select a model on Qap data.

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