Semi-supervised segmentation within a predictive modelling context

Faculty of Natural Sciences Centre for Business Mathematics and Informatics Doctor of Philosophy Semi-supervised segmentation within a predictive modelling context by Douw Gerbrand Breed Industry standards and best practices on robust model development have been refined over many years. Even though many software tools are available to simplify the process today, developing a practically implementable model for long-term use still involves substantial human input. Subsequently, any methodologies that aid in the improvement of model accuracy or increase the efficiency with which models can be developed is welcomed by all involved. Segmentation of the data that are used for predictive modelling is a well-established practice in the industry. Segmentation of subjects (i.e. observations or customers) is defined in this study as partitioning of the subjects into distinct groups, or subsets, with the aim of developing predictive models on each of the groups separately. The focus of our study will be on broadening the available techniques that can be used for statistical segmentation. Currently two main streams of statistical segmentation exist in the industry, namely unsupervised and supervised segmentation. Both these streams make intuitive sense, depending on the application and the requirements of the models developed, and many examples exist where the use of either technique improved model performance. However, both these streams focus on a single aspect (i.e. either target separation or explanatory variable distribution) and combining both aspects might deliver better results in some instances. The primary objective of this research is to develop and define a semi-supervised segmentation algorithm as an alternative to the segmentation algorithms currently in use. This algorithm should allow the user, when segmenting for predictive modelling, to not only consider the explanatory variables (as is the case with unsupervised techniques such as clustering) or the target variable (as is the case with supervised techniques such as decision trees), but to be able to optimise both simultaneously during the segmentation exercise. Once we have defined the semi-supervised segmentation algorithm that is based on standard k-means clustering, we comprehensively analyse it by applying it in several different ways. We illustrate visually how the algorithm differs from standard k-means clustering and how it is able to overcome some of the known weaknesses of k-means clustering. We apply the algorithm to actual data sets from various industries and compare the results to results of other known segmentation algorithms on the same data sets. A number of popular non-linear modelling techniques are also applied to the data sets to compare the accuracy of those techniques to the accuracy obtained with the various segmentation techniques. Simulated data serve to identify a few key data set characteristics that may cause one segmentation technique to outperform another. In addition, we define data set characteristics that suit the semi-supervised segmentation technique best. Finally, we propose two alternative semi-supervised segmentation techniques and measure how these techniques perform on the industry data sets already analysed. We, furthermore, augment a supervised clustering technique found in literature and compare its results to all other results obtained.