A Review of KDD-Data Mining Framework and Its Application in Logistics and Transportation

Fauziah Abdul Rahman
Siti Maryam Shamsuddin (Universiti Teknologi Malaysia(UTM))
Shafaatunnur Hassan (Universiti Teknologi Malaysia(UTM))
Norhaidah Abu Haris (Universiti Kuala Lumpur, Malaysian Institute of Information Technology(MIIT))

International Journal of Supply Chain Management
Vol 5, No 2 (2016): International Journal of Supply Chain Management (IJSCM)

In this paper, an understanding and a review of Knowledge Discovery Database 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 fact 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. The paper is concluded by proposing a framework for actionable knowledge discovery and data mining to be applicable in real life application such as within the context of transportation industry. Keywords Data Mining, Knowledge Discovery Database-Data Mining (KDD-DM), Domain Driven Data Mining (DDDM) Knowledge Discovery, Domain Driven Data Mining- Actionable Knowledge Discovery (AKD-KDD), Logistics, Fleet Maintenance.