New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry

This thesis investigates profiling and differentiating customers through the use of statistical data mining techniques. The business application of our work centres on examining individuals’ seldomly studied yet critical consumption behaviour over an extensive time period within the context of the wireless telecommunication industry; consumption behaviour (as oppose to purchasing behaviour) is behaviour that has been performed so frequently that it become habitual and involves minimal intentions or decision making. Key variables investigated are the activity initialised timestamp and cell tower location as well as the activity type and usage quantity (e.g., voice call with duration in seconds); and the research focuses are on customers’ spatial and temporal usage behaviour. The main methodological emphasis is on the development of clustering models based on Gaussian mixture models (GMMs) which are fitted with the use of the recently developed variational Bayesian (VB) method. VB is an efficient deterministic alternative to the popular but computationally demandingMarkov chainMonte Carlo (MCMC) methods. The standard VBGMMalgorithm is extended by allowing component splitting such that it is robust to initial parameter choices and can automatically and efficiently determine the number of components. The new algorithm we propose allows more effective modelling of individuals’ highly heterogeneous and spiky spatial usage behaviour, or more generally human mobility patterns; the term spiky describes data patterns with large areas of low probability mixed with small areas of high probability. Customers are then characterised and segmented based on the fitted GMM which corresponds to how each of them uses the products/services spatially in their daily lives; this is essentially their likely lifestyle and occupational traits. Other significant research contributions include fitting GMMs using VB to circular data i.e., the temporal usage behaviour, and developing clustering algorithms suitable for high dimensional data based on the use of VB-GMM.

Feasibility studies on using microwave-heating techniques for recycling lead waste

Lead is present everywhere in the environment and has been defined as one of the greatest threats to the human health. In this paper, attempts have been made to study a way of recycling the lead produced from waste usage and disposed of in such a way as to avoid degrading the surrounding environment. In order to contain the waste, recycled asphalt material is mixed with the lead and then heated with microwave energy. This is an attempt to solidify and reduce the lead contaminants and use the final product as sub-base material in road pavement construction. The microwave heating of the specimens is carried out with 30%, 50%, 80% and 100% of power at 800W. The optimum power mode is used to compare with the conventional heating of asphalt with sulfur additive. The results are characterized by compact density, permeability, and subjected to toxicity test with regards to lead concentration. A mechanical test to evaluate the stability is also performed on the three methods of solidification and to prove that microwave zapping method allow to convert into an environmentally stable material for recycling without having to be deposited in a landfill site.