Business intelligence problems are particularly challenging due to the use of large volume and high velocity data in attempts to model and explain complex underlying phenomena. Incremental machine learning based approaches for summarizing trends and identifying anomalous behavior are often desirable in such conditions to assist domain experts in characterizing their data. The overall goal of this research is to develop a machine learning algorithm that enables predictive analysis on streaming data, detects changes and anomalies in the data, and can evolve based on the dynamic behavior of the data. Commercial shipping transaction data for the U.S. is used to develop and test a Naïve Bayes model that classifies several companies into lines of businesses and demonstrates an ability to predict when the behavior of these companies changes by venturing into other lines of businesses.
Revised: December 2, 2016 |
Published: September 30, 2015
Citation
Bramer L.M., S. Chatterjee, A.E. Holmes, S.M. Robinson, S.F. Bradley, and B.M. Webb-Robertson. 2015.A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data. In The 11th International Conference on Data Mining (DMIN 2015), July 27-30, 2015, Las Vegas, Nevada, 162-167. Athens, Georgia:CSREA Press.PNNL-SA-110014.