In the field of Data Mining, association rule mining is one of the most popular techniques
to discover interesting, non-obvious rules from frequent patterns inside the dataset being analyzed.
Commonly used for market basket analysis, the present study aims to use this technique to discover
interesting rules in applied Process and Enterprise Maturity Models (Hammer, 2007) on Taiwanese
companies in recent years. The application of PEMM consists on an evaluation performed by
company managers; its purpose is to identify the process and enterprise maturity level in terms of
process orientation. The result of the PEMM analysis is arranged in a matrix fashion, and a
supporting text explains the achieved maturity level. In addition of the previously mentioned
Association Rule Mining method; a text ranking technique is proposed to analyze the provided
text to look for success, opportunities and failure key words in Taiwanese companies pursuing
process oriented maturity levels.
The present research is carried out with the input of twelve Taiwanese companies (six midsized and six big-sized companies) coming from diverse industries, such as, Pharmaceutical, Hightech, Automotive, and Semiconductors/OEM. Finally, a comparison between the mid and bigsized companies’ analysis is carried out aiming to obtain valuable insights from these maturity
models by using the aforementioned techniques. The KDD framework is used to facilitate its
usability as a benchmark tool (an aid to retrieve useful information from implementations of the
PEMM); which in turn becomes a knowledge asset for companies.
Results from this study show that analytical methods such as ARM “Apriori” algorithm
and Text Analysis under the Data Mining field are applicable to discover non-obvious information
from applied PEMM assessments.

In the field of Data Mining, association rule mining is one of the most popular techniques
to discover interesting, non-obvious rules from frequent patterns inside the dataset being analyzed.
Commonly used for market basket analysis, the present study aims to use this technique to discover
interesting rules in applied Process and Enterprise Maturity Models (Hammer, 2007) on Taiwanese
companies in recent years. The application of PEMM consists on an evaluation performed by
company managers; its purpose is to identify the process and enterprise maturity level in terms of
process orientation. The result of the PEMM analysis is arranged in a matrix fashion, and a
supporting text explains the achieved maturity level. In addition of the previously mentioned
Association Rule Mining method; a text ranking technique is proposed to analyze the provided
text to look for success, opportunities and failure key words in Taiwanese companies pursuing
process oriented maturity levels.
The present research is carried out with the input of twelve Taiwanese companies (six midsized and six big-sized companies) coming from diverse industries, such as, Pharmaceutical, Hightech, Automotive, and Semiconductors/OEM. Finally, a comparison between the mid and bigsized companies’ analysis is carried out aiming to obtain valuable insights from these maturity
models by using the aforementioned techniques. The KDD framework is used to facilitate its
usability as a benchmark tool (an aid to retrieve useful information from implementations of the
PEMM); which in turn becomes a knowledge asset for companies.
Results from this study show that analytical methods such as ARM “Apriori” algorithm
and Text Analysis under the Data Mining field are applicable to discover non-obvious information
from applied PEMM assessments.

Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large
databases. In Acm sigmod record (Vol. 22, No. 2, pp. 207-216). ACM.
Bartmann, D., Rajola, F., Kallinikos, J., Avison, D., Winter, R., and Ein-Dor, P. (2005). Thinker Tailor:
Information systems and strategic development in Knowledge-based SMEs. Thirteenth European
Conference on Information Systems. Germany, Regensburg.
Burton, Scott H., Morris, Richard G., Giraud-Carrier, Christophe G., West, Joshua H. and Thackeray,
Rosemary (2014) Mining useful association rules from questionnaire data. Journal Intelligent Data
Analysis, V18 3: pp 479-494.
De Bruin, Tonia and Rosemann, Michael (2005). Towards a Business Process Management Maturity Model.
Queensland University of Technology. Working paper.
Eibe Frank, Mark A. Hall, and Ian H. Witten (2016). The WEKA Workbench. Online Appendix for "Data
Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition,
2016.
Fa, Weiguo., Wallace, Linda. Rich, Stephanie and Zhang, Zhongju (2006). Tapping the power of the text
mining. Communications of the ACM, V49 9: pp 76-82.
Fan, W., Wallace, L., Rich, S., & Zhang, Z. (2006). Tapping the power of text mining. Communications of
the ACM, 49(9), 76-82.
Feinerer, Ingo (2008) A text mining framework in R and its applications. Doctoral thesis, WU Vienna
University of Economics and Business.
Fieldman, R. and Daga, I. (1995). Proceedings of the First International Conference on Knowledge
Discovery in Data Mining. KDD-95, pp 112-117.
Frawley, William J., Piatetsky-Shapiro, Gregory and Matheus, Christoper J. (1992) Knowledge Discovery
in Databases: An Overview. Artificial Intelligence. V13:3
Garner Report (2005). Delivering IT’s Contribution: The 2005 CIO Agenda. EXP Premier Report, January.
Hammer, Michael (2007. The Process Audit, Harvard Business Review.
Han, Jiawei and Kamber, Micheline (2006). Data Mining: Concepts and Techniques. 2nd. Edition.
Hearst, M. A. (1999, June). Untangling text data mining. In Proceedings of the 37th annual meeting of the
Association for Computational Linguistics on Computational Linguistics (pp. 3-10). Association
for Computational Linguistics.
Huang, A. (2008, April). Similarity measures for text document clustering. In Proceedings of the sixth New
Zealand computer science research student conference (NZCSRSC2008), Christchurch, New60
Zealand (pp. 49-56).
Mandave, Pratibha., Mane, Megha and Patil, Sharada (1993). Data mining using Association rule based on
APRIORI algorithm and improved approach with illustration. International Journal of Latest
Trends in Engineering and Technology. V3 2: pp 107-113."
Medrano, Martín (2013), Evaluación de la gobernanza TIC por controles COBIT utilizando técnicas de
decisión multicriterio difusas e híbridas, (Master Thesis), UNICAN.
Mierswa, Ingo., Wurst, Michael., Klinkenberg, Ralf., Scholz, Martin and Euler, Timm (2006) Yale: Rapid
prototyping for complex data mining tasks. In Proceedings of the 12th ACM SIGKDD international
conference on Knowledge discovery and data mining, pages 935–940. ACM.
Narin, Francis., Hamilton, Konberly S. and Olivastro, Dominic (1997) The increasing linkage between U.S.
technology and public science. Research Policy, pp 317-330.
OECD Report (2005). OECD SME and Entrepreneurship Outlook: 2005, OECD Paris, pp 17.
Ramos, J. (2003). Using TF-IDF to determine word relevance in document queries. In Proceedings of the
first instructional conference on machine learning (Vol. 242, pp. 133-142).
Rexer Analytics (2011). Annual Rexer Analytics Data Miner Survey. Survey Highlights:
http://www.rexeranalytics.com/Data-Miner-Survey-Results-2011.html
Roglinger, Maximilian, Poppelbub, Jens and Becker, Jorg (2012). Maturity models in business process
management. Business Process Management Journal. V18 2: pp 328-346
Spanyi, Andrew (2014). Handbook on Business Process Management. The Governance of Business Process
Management, pp 333-349, 2015
Swanson, Don R. and Smalheiser, Neil R. (1997). An interactive system for finding complementary
literatures: a stimulus to scientific discovery. Artificial Intelligence, 91:183-203.
Tarhan, Ayca, Turetken, Oktay and Reijers, Hajo A. (2016). Business process maturity models: A systematic
literature review. Information and Software Technology pp 122-134
Tukey, John W. (1977). Explanatory Data analysis. Addison-Wesley Publishing Company.
Von Brocke, Jan and Rosemann, Michael (2010). Handbook on Business Process Management 2. 2nd
Edition pp 342.
Yan, X., Cheng, H., Han, J., & Xin, D. (2005, August). Summarizing itemset patterns: a profile-based
approach. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge
discovery in data mining (pp. 314-323). ACM.
Zaki, M. J. (2004). Mining non-redundant association rules. Data mining and knowledge discovery, 9(3),
223-248.
Zytkow, J.M. and Quafafou, M. (1998). Principles of Data Mining and Knowledge Discovery. Second
European Symposium, PKDD'98, Nantes,