Computing and Information Systems - Theses
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ItemCluster validation and discovery of multiple clusterings( 2016)Cluster analysis is an important unsupervised learning process in data analysis. It aims to group data objects into clusters, so that the data objects in the same group are more similar and the data objects in different groups are more dissimilar. There are many open challenges in this area. In this thesis, we focus on two: discovery of multiple clusterings and cluster validation. Many clustering methods focus on discovering one single ‘best’ solution from the data. However, data can be multi-faceted in nature. Particularly when datasets are large and complex, there may be several useful clusterings existing in the data. In addition, users may be seeking different perspectives on the same dataset, requiring multiple clustering solutions. Multiple clustering analysis has attracted considerable attention in recent years and aims to discover multiple reasonable and distinctive clustering solutions from the data. Many methods have been proposed on this topic and one popular technique is meta-clustering. Meta-clustering explores multiple reasonable and distinctive clusterings by analyzing a large set of base clusterings. However, there may exist poor quality and redundant base clustering which will affect the generation of high quality and diverse clustering views. In addition, the generated clustering views may not all be relevant. It will be time and energy consuming for users to check all the returned solutions. To tackle these problems, we propose a filtering method and a ranking method to achieve higher quality and more distinctive clustering solutions. Cluster validation refers to the procedure of evaluating the quality of clusterings, which is critical for clustering applications. Cluster validity indices (CVIs) are often used to quantify the quality of clusterings. They can be generally classified into two categories: external measures and internal measures, which are distinguished in terms of whether or not external information is used during the validation procedure. In this thesis, we focus on external cluster validity indices. There are many open challenges in this area. We focus two of them: (a) CVIs for fuzzy clusterings and, (b) Bias issues for CVIs. External CVIs are often used to quantify the quality of a clustering by comparing it against the ground truth. Most external CVIs are designed for crisp clusterings (one data object only belongs to one single cluster). How to evaluate the quality of soft clusterings (one data object can belong to more than one cluster) is a challenging problem. One common way to achieve this is by hardening a soft clustering to a crisp clustering and then evaluating it using a crisp CVI. However, hardening may cause information loss. To address this problem, we generalize a class of popular information-theoretic based crisp external CVIs to directly evaluate the quality of soft clusterings, without the need for a hardening step. There is an implicit assumption when using external CVIs for evaluating the quality of a clustering, that is, they work correctly. However, if this assumption does not hold, then misleading results might occur. Thus, identifying and understanding the bias behaviors of external CVIs is crucial. Along these lines, we identify novel bias behaviors of external CVIs and analyze the type of bias both theoretically and empirically.
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ItemDesign and adjustment of dependency measures( 2015)Dependency measures are fundamental for a number of important applications in data mining and machine learning. They are ubiquitously used: for feature selection, for clustering comparisons and validation, as splitting criteria in random forest, and to infer biological networks, to list a few. More generally, there are three important applications of dependency measures: detection, quantification, and ranking of dependencies. Dependency measures are estimated on finite data sets and because of this the tasks above become challenging. This thesis proposes a series of contributions to improve performances on each of these three goals. When differentiating between strong and weak relationships using information theoretic measures, the variance plays an important role: the higher the variance, the lower the chance to correctly rank the relationships. In this thesis, we discuss the design of a dependency measure based on the normalized mutual information whose estimation is based on many random discretization grids. This approach allows us to reduce its estimation variance. We show that a small estimation variance for the grid estimator of mutual information if beneficial to achieve higher power when the task is detection of dependencies between variables and when ranking different noisy dependencies. Dependency measure estimates can be high because of chance when the sample size is small, e.g. because of missing values, or when the dependency is estimated between categorical variables with many categories. These biases cause problems when the dependency must have an interpretable quantification and when ranking dependencies for feature selection. In this thesis, we formalize a framework to adjust dependency measures in order to correct for these biases. We apply our adjustments to existing dependency measures between variables and show how to achieve better interpretability in quantification. For example, when a dependency measure is used to quantify the amount of noise on functional dependencies between variables, we experimentally demonstrate that adjusted measures have more interpretable range of variation. Moreover, we demonstrate that our approach is also effective to rank attributes during the splitting procedure in random forests where a dependency measure between categorical variables is employed. Finally, we apply our framework of adjustments to dependency measures between clusterings. In this scenario, we are able to analytically compute our adjustments. We propose a number of adjusted clustering comparison measures which reduce to well known adjusted measures as special cases. This allows us to propose guidelines for the best applications of our measures as well as for existing ones for which guidelines are missing in literature, e.g. for the Adjusted Rand Index (ARI).
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ItemRecommendation systems for travel destination and departure time( 2015)People travel on a daily basis to various local destinations such as office, home, restaurant, appointment venue, and sightseeing spot. It is vital to most people that we have a positive experience and high efficiency of daily travel. With this observation, my research strives to provide daily-travel related recommendations by solving two optimisation problems, driving destination prediction and departure time recommendation for appointments. Our “SubSyn” destination prediction algorithm, by definition, predicts potential destinations at real-time for drivers on the road. Its applications include recommending sightseeing places, pushing targeted advertisement, and providing early warnings for road congestion. It employs the Bayesian inference framework and second-order Markov model to compute a list of high-probability destinations. The key contributions include real-time processing and the ability to predict destinations with very limited amount of training data. We also look into the problem of privacy protection against such prediction. The “iTIME” departure time recommendation system is a smart calendar that reminds users to depart in order to arrive at appointment venues on time. It also suggests the best transport mode based on users’ travel history and preferences. Currently, it is very inefficient for people to manually and repeatedly check the departure time and compare all transport modes using, for instance, Google Maps. The functionalities of iTIME were realised by machine learning algorithms that learn users’ habits, analyse the importance of appointments and optimal mode of transport, and estimate the start location and travel time. Our field study showed that we can save up to 40% of time by using iTIME. The system can also be extended easily to provide additional functionalities such as clashing appointments detection and appointment scheduling, both taking into account the predicted start location and travel time of future appointments. Both problems can be categorised as recommender systems (or recommendation systems) that provide insightful suggestions in order to improve daily-travel experiences and efficiency.
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ItemReal-time feedback for surgical simulation using data mining( 2014)Surgical trainees devote years to master the surgical skills required to safely perform surgery. Traditionally, they refine their psychomotor skills by practising on plastic bones or cadavers under the supervision of expert surgeons. Experts guide trainees through surgical procedures while providing feedback on the quality of their procedure. However, there are limitations to this approach, which include a shortage of cadaver bones, limited availability of expert supervision, and the subjective manner of surgical skill assessment. To address these limitations, the introduction of new techniques such as 3D illusion, haptic feedback and augmented reality have significantly improved the realism of surgical simulators. Such simulators have the potential to provide a cost-effective platform, which allows trainees to practice many surgical cases of varying difficulty, and provides the flexibility of practising repeatedly at their own convenience. However, most simulators lack the automated performance assessment and feedback, which limits the applicability of simulators as self-guided training systems. In thesis, we aim to deliver automated performance assessment and feedback in a virtual simulation environment. The automated performance assessment provides information on the quality of surgical result, which is a critical component for a self-guided training platform. A large number of recent studies have focused on scoring the outcome of surgical tasks. However, this score typically based on the result of a surgical task (such as the shape of a surgical end-product) and ignores the rich information provided by real-time performance attributes, such as motion records. Furthermore, since this assessment is delivered at the end of each task, it does not allow any opportunity to identify and address mistakes as they occur. We propose an event-based framework that provides online assessment with different temporal granularities. The evaluations show that the proposed framework provides accurate performance assessment using both motion records and end-product information. Although automated performance assessment provides expertise score to illustrate the surgical perform, a single score has limited utility in improving surgical technique, which can be equally important. Trainees need constructive human understandable feedback to refine their psychomotor skills. To this end, we propose a Random Forest based approach to generate meaningful automated real-time performance feedback. Our evaluation demonstrates it can significantly improve the surgical techniques. However, this random forest based method makes specific assumptions that all drilling movements made by experts are of "expert quality'' and all operations made by trainees are suboptimal. This hampers the model training process and leads to lower accuracy rates. Therefore we propose a pattern-based approach to capture the differences in technique between experts and trainees and deliver real-time feedback to improve performance, while avoiding the assumption of "polarising'' the quality of drill strokes based on expertise. Our evaluation results show that the proposed approach identifies the stage of the surgical procedure correctly and provides constructive feedback to assist surgical trainees in improving their technique. Another challenge for automated performance assessment is hard to extend existing evaluation models to new specimens. In order to train reliable assessment models for new specimens, the classical machine learning approaches require a new set of human expert examples collected from each new specimen. To eliminate this need, we propose a transfer learning framework to adapt a classifier built on a single specimen to multiple specimens. Once a classifier is trained, we translate the new specimens' features to the original feature space, which allows us to carry out performance evaluation on different specimens using the same classifier. In summary, the major contributions of this thesis involve the development of self-guided training platform for delivering automatic assessment and feedback using data mining techniques.
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ItemDistribution enhanced data mining methods for time series forecasting( 2014)Time series forecasting is an exciting research area whose challenges are to discover patterns from data that has been observed over time. Whether it is stock market variables concerning price behaviour, or weather measurements that can be used to issue a timely warning of an approaching cyclone, or radiation level measurements that can prevent a harmful disaster and save hundreds of lives in a power plant, time series find useful applications in many diverse sciences and disciplines. The behaviour of time series is susceptible to changes, often reflected through distribution features such as mean and variance. Though changes in the series are to be expected, they are of mostly a continuous nature, meaning that predictions to a certain point in the future can be made with a high degree of accuracy. Understanding how these changes affect the prediction accuracy is crucial to minimizing the forecasted error. This thesis investigates utilising information about changes in the series, and presents novel modifications in the learning process based on the knowledge gained about these changes. There are four main contributions presented in this thesis, which deliver innovative techniques for time series analysis by incorporating distribution characteristics. In the first part of the thesis we develop a pre-processing algorithm that uses distribution information which can easily accompany any prediction model we choose to work with. Our algorithm performs a fast and efficient reduction of the training samples depending on the change of the mean of the distribution, leaving a set of samples with a larger concentration of useful information and reduced noise. In the next part of our work, we introduce an intelligent group-based error minimization algorithm, which simultaneously achieves reduction of both mean and variance of the forecasted errors, associated with groups of observations with similar distribution. We demonstrate how this sensitive grouping of samples reduces both the error and variance of the error per group, embodied in a modified linear regression algorithm. We then introduce a modified form of Support Vector Regression that detects potential large-error producing samples, and which penalizes the loss for these samples by using a time-sensitive loss, for directly targeting a reduction of the variance in the forecasted errors. This new approach achieves competitive reduction in the error variance and produces more accurate predictions, with performance better than several state of the art methods. Finally, we apply our technical methodologies for the purpose of discrimination aware learning, where we demonstrate how they can be modified and used in another research context.