A. Zarei - Faculty of Economic, Management and Administrative Sciences, Semnan University, Semnan, Iran.
M. Maleki - Faculty of Economic, Management and Administrative Sciences, Semnan University, Semnan, Iran.
D. Feiz - Faculty of Economic, Management and Administrative Sciences, Semnan University, Semnan, Iran.
M. A. Siahsarani kojuri - Faculty of Economic, Management and Administrative Sciences, Semnan University, Semnan, Iran.

Competitive intelligence (CI) has become one of the major subjects for researchers in recent years. The present research is aimed to achieve a part of the CI by investigating the scientific articles on this field through text mining in three interrelated steps. In the first step, a total of 1143 articles released between 1987 and 2016 were selected by searching the phrase competitive intelligence in the valid databases and search engines; then, through reviewing the topic, abstract, and main text of the articles as well as screening the articles in several steps, the authors eventually selected 135 relevant articles in order to perform the text mining process. In the second step, pre-processing of the data was carried out. In the third step, using non-hierarchical cluster analysis (k-means), 5 optimum clusters were obtained based on the Davies–Bouldin index, for each of which a word cloud was drawn; then, the association rules of each cluster was extracted and analyzed using the indices of support, confidence, and lift. The results indicated the increased interest in researches on CI in recent years and tangibility of the strong and weak presence of the developed and developing countries in formation of the scientific products; further, the results showed that information, marketing, and strategy are the main elements of the CI that, along with other prerequisites, can lead to the CI and, consequently, the economic development, competitive advantage, and sustainability in market.

Competitive Intelligence, Text mining, Association rules mining, Word cloud