Atlantic Tropical Cyclone Intensity Forecasting via the Minimum Message Length Principle: A Preliminary Result

presented at the 22nd Conference on Hurricane and Tropical Meteorology, Fort Collins -- Colorado, 19 -- 23 May 1997 1 Introduction The existing tropical cyclone intensity forecasting schemes (SHIFOR [Jarvinen and Neumann, 1979], SHIPS [DeMaria and Kaplan, 1994], SHIFOR94 [Landsea, 1995], TIPS [Fitzpatrick, 1995]) were built using the conventional multiple linear regression method. This method relies upon statistical significance test techniques which the chosen models prone to overfit the data. This inherent tendency of overfitting makes the separation of the limited available data into the training and test data sets imperative. In this abstract, a Bayesian approach using the Minimum Message Length (MML) principle [Wallace and Freeman, 1987] is applied to tropical cyclone intensity change forecasting. The MML technique builds regression models by taking a balance between the complexity of the models and the goodness of fit as a performance criterion. Because of this balancing mech..

Two-tiered Clustering Classification Experiments for Market Segmentation of EFTPOS Retailers

This paper proposes the application of clustering and classification techniques on finding groupings of retailers who use the Electronic Funds Transfer at Point Of Sale (EFTPOS) facilities of a major bank in Australia. The RFM (Recency, Frequency, Monetary) analysis on each retailer is used to reduce the large data set of customer purchases through the EFTPOS network for the purpose of the retailer clustering. We then incorporate attributes of the EFTPOS transaction data in addition to the derived RFM attributes to build a decision tree to facilitate the extraction of business rules that explain the characteristics of the retailer clusters