Knowledge Discovery Database (KDD)-Data Mining Application in Transportation

Fauziah Abdul Rahman, Mohammad Ishak Desa, Antoni Wibowo, Norhaidah Abu Haris


In this paper, an understanding and a review of data mining (DM) development and its applications in logistics and specifically transportation are highlighted. Even though data mining has been successful in becoming a major component of various business processes and applications, the benefits and real-world expectations are very important to consider. It is also surprising to note that very little is known to date about the usefulness of applying data mining in transport related research. From the literature, the frameworks for carrying out knowledge discovery and data mining have been revised over the years to meet the business expectations. In this paper, we apply CRISP-DM for formulating effective tire maintenance strategy within the context of a Malaysian’s logistics company. The results of applying CRISP-DM for tire maintenance decisions are presented and discussed.


Data Mining; Knowledge Discovery Database- Data Mining (KDD-DM); Domain-Driven Data Mining (DDDM); Actionable Knowledge Discovery (AKD); Logistics and Transportation


Dan Luo, Longbing Cao, Chao Luo, Chengqi Zhang and Weiyuan Wang, 2008. Towards Business Interestingness in Actionable Knowledge Discovery. Proceeding of the 2008 conference on Applications of Data Mining in E-Business and Finance(IOS Press Amsterdam, The Netherlands, The Netherlands, 2008). DOI=

Cao Longbing and Zhang Chengqi. 2007. The Evolution of KDD: Towards Domain-Driven Data Mining, . International Journal of Pattern Recognition and Artificial Intelligence, 21(4), 677-692.

Longbing Cao, 2008. Domain Driven Data Mining: Challenges and Prospects. Journal on Knowledge and Data Engineering. 6 (22)(June 2010), 755-769.

Kalaivany Natarajan, Jiuyong Li and Andy Koronios,2009. Data Mining Techniques or Data Cleaning. In Proceedings of the 4th World Congress on Engineering Asset Management (Athens, Greece, 28 – 30, September). DOI=

R.KAVITHA KUMAR and et.el . 2011.Attribute Correction-Data Cleaning using Association Rule and Cluestering Method. International Journal of Data Mining & Knowledge Management Process (IJDKP. 1(2)(March 2011), 22-32.

Sang Jun Lee and et. el. 2001. A Review of Data Mining Technique, Industrial Management & Data Systems,101 (1).41-46.

Daniel T.Larose. 2005. Discovering Knowledge in Data:An introduction on Data Mining. Book. 27-65.

Hasimah Hj Mohamed and et. el. 2011. E-Clean: A Data Cleaning Framework for Patient Data. 2011. First International Conference on Informatics and Computational Intelligence. DOI=

Erhard Rahm, Hong Hai Do. 2000. Data Cleaning: Problems and Current Approaches. IEEE Data(base) Engineering Bulletin - DEBU Journal,. 23( 4). 3-13.

Lior Rokach and et. el. 2010. Data Mining with Decision Trees:Theory and Applications. World Scientific Publishing Co. Pte. Ltd. 1-214.

Paolo Giudici et. el.Applied Data Mining for Business and Industry. 2010. Johd Wiley & Sons Ltd. 219-225.

Alex A. Freitas. Data Mining and Knowledge Discovery with Evolutionary Algorithms. 2004. Springer. 65-75.

Tamraparni Dasu and et. el. Explorattory Data Mining and Data Cleaning. 2004. A John Wiley &Sons, Inc., Publication. 99-189.

Tsau Youn Lin and et. el.. Direct Data Mining of Rules from Data with Missing Values. 2005. Springer.233-264.

Longbing Cao.2008. Introduction to Domain Driven Data Mining. Data Mining For Business Application. Springer 2009.

Longbing Cao and Chengqi Zhang.2007. The Evolution of KDD: Towards Domain-Driven Data Mining. International Journal of Pattern Recognition and Artificial Intelligence. Vol. 21. No. 4 (2007). Pages 677-692.

Lily Sun, Cleopa John Mushi.2010. Case-based analysis in user requirements modeling for knowledge construction. Elsevier.

P. Haluzova, 2008. Effective Data Mining for a Transportation Information Systems. Czech Technical University Publishing

Rayid Ghani and Carlos Soares . 2006. KDD-2006 Workshop.Accenture Technology Labs.

Sudhir Kumar Barai. 2003. Data Mining Applications in Transportation Engineering. India Institute of Technology Kharagpur, India.

U.S Pattern Storm.2001.Vehicle Maintenance Management System and Method.

Thomas Young and Data Mining to Influence Maintenance Actions.

WenQun Wang, Haibo and Magaret. 2004. Vehicle Breakdown Duration Modeling. Journal of Transportation and Statistics.

William R. King, Peter V. Marks, Jr., and Scott McCoy. 2002. The Most Important Issues in Knowledge Management. Communications Of the ACM, September 2002 Vol. 45 No 49.

Zhengxiang and Research on Domain-Driven Actionable Knowledge Discovery. Springer 2009.

Full Text: PDF


  • There are currently no refbacks.