Advances in Big Data Analytics: Algorithmic Stability and Data Cleansing

Analysis of what has come to be called “big data” presents a number of challenges as data continues to grow in size, complexity and heterogeneity. To help addresses these challenges, we study a pair of foundational issues in algorithmic stability (robustness and tuning), with application to clustering in high-throughput computational biology, and an issue in data cleansing (outlier detection), with application to pre-processing in streaming meteorological measurement. These issues highlight major ongoing research aspects of modern big data analytics. First, a new metric, robustness, is proposed in the setting of biological data clustering to measure an algorithm’s tendency to maintain output coherence over a range of parameter settings. It is well known that different algorithms tend to produce different clusters, and that the choice of algorithm is often driven by factors such as data size and type, similarity measure(s) employed, and the sort of clusters desired. Even within the context of a single algorithm, clusters often vary drastically depending on parameter settings. Empirical comparisons performed over a variety of algorithms and settings show highly differential performance on transcriptomic data and demonstrate that many popular methods actually perform poorly. Second, tuning strategies are studied for maximizing biological fidelity when using the well-known paraclique algorithm. Three initialization strategies are compared, using ontological enrichment as a proxy for cluster quality. Although extant paraclique codes begin by simply employing the first maximum clique found, results indicate that by generating all maximum cliques and then choosing one of highest average edge weight, one can produce a small but statistically significant expected improvement in overall cluster quality. Third, a novel outlier detection method is described that helps cleanse data by combining Pearson correlation coefficients, K-means clustering, and Singular Spectrum Analysis in a coherent framework that detects instrument failures and extreme weather events in Atmospheric Radiation Measurement sensor data. The framework is tested and found to produce more accurate results than do traditional approaches that rely on a hand-annotated database

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Unsupervised learning for anomaly detection in Australian medical payment data

Fraudulent or wasteful medical insurance claims made by health care providers are costly for insurers. Typically, OECD healthcare organisations lose 3-8% of total expenditure due to fraud. As Australia’s universal public health insurer, Medicare Australia, spends approximately $A 34 billion per annum on the Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme, wasted spending of$A1–2.7 billion could be expected.However, fewer than 1% of claims to Medicare Australia are detected as fraudulent, below international benchmarks. Variation is common in medicine, and health conditions, along with their presentation and treatment, are heterogenous by nature. Increasing volumes of data and rapidly changing patterns bring challenges which require novel solutions. Machine learning and data mining are becoming commonplace in this field, but no gold standard is yet available. In this project, requirements are developed for real-world application to compliance analytics at the Australian Government Department of Health and Aged Care (DoH), covering: unsupervised learning; problem generalisation; human interpretability; context discovery; and cost prediction. Three novel methods are presented which rank providers by potentially recoverable costs. These methods used association analysis, topic modelling, and sequential pattern mining to provide interpretable, expert-editable models of typical provider claims. Anomalous providers are identified through comparison to the typical models, using metrics based on costs of excess or upgraded services. Domain knowledge is incorporated in a machine-friendly way in two of the methods through the use of the MBS as an ontology. Validation by subject-matter experts and comparison to existing techniques shows that the methods perform well. The methods are implemented in a software framework which enables rapid prototyping and quality assurance. The code is implemented at the DoH, and further applications as decision-support systems are in progress. The developed requirements will apply to future work in this fiel

A Machine Learning Technique for Detection of Social Media Fake News

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Behaviour Profiling using Wearable Sensors for Pervasive Healthcare

In recent years, sensor technology has advanced in terms of hardware sophistication and miniaturisation. This has led to the incorporation of unobtrusive, low-power sensors into networks centred on human participants, called Body Sensor Networks. Amongst the most important applications of these networks is their use in healthcare and healthy living. The technology has the possibility of decreasing burden on the healthcare systems by providing care at home, enabling early detection of symptoms, monitoring recovery remotely, and avoiding serious chronic illnesses by promoting healthy living through objective feedback. In this thesis, machine learning and data mining techniques are developed to estimate medically relevant parameters from a participant‘s activity and behaviour parameters, derived from simple, body-worn sensors. The first abstraction from raw sensor data is the recognition and analysis of activity. Machine learning analysis is applied to a study of activity profiling to detect impaired limb and torso mobility. One of the advances in this thesis to activity recognition research is in the application of machine learning to the analysis of 'transitional activities': transient activity that occurs as people change their activity. A framework is proposed for the detection and analysis of transitional activities. To demonstrate the utility of transition analysis, we apply the algorithms to a study of participants undergoing and recovering from surgery. We demonstrate that it is possible to see meaningful changes in the transitional activity as the participants recover. Assuming long-term monitoring, we expect a large historical database of activity to quickly accumulate. We develop algorithms to mine temporal associations to activity patterns. This gives an outline of the user‘s routine. Methods for visual and quantitative analysis of routine using this summary data structure are proposed and validated. The activity and routine mining methodologies developed for specialised sensors are adapted to a smartphone application, enabling large-scale use. Validation of the algorithms is performed using datasets collected in laboratory settings, and free living scenarios. Finally, future research directions and potential improvements to the techniques developed in this thesis are outlined

Artificial Intelligence for Data Analysis and Signal Processing

Artificial intelligence, or AI, currently encompasses a huge variety of fields, from areas such as logical reasoning and perception, to specific tasks such as game playing, language processing, theorem proving, and diagnosing diseases. It is clear that systems with human-level intelligence (or even better) would have a huge impact on our everyday lives and on the future course of evolution, as it is already happening in many ways. In this research AI techniques have been introduced and applied in several clinical and real world scenarios, with particular focus on deep learning methods. A human gait identification system based on the analysis of inertial signals has been developed, leading to misclassification rates smaller than 0.15%. Advanced deep learning architectures have been also investigated to tackle the problem of atrial fibrillation detection from short length and noisy electrocardiographic signals. The results show a clear improvement provided by representation learning over a knowledge-based approach. Another important clinical challenge, both for the patient and on-board automatic alarm systems, is to detect with reasonable advance the patterns leading to risky situations, allowing the patient to take therapeutic decisions on the basis of future instead of current information. This problem has been specifically addressed for the prediction of critical hypo/hyperglycemic episodes from continuous glucose monitoring devices, carrying out a comparative analysis among the most successful methods for glucose event prediction. This dissertation also shows evidence of the benefits of learning algorithms for vehicular traffic anomaly detection, through the use of a statistical Bayesian framework, and for the optimization of video streaming user experience, implementing an intelligent adaptation engine for video streaming clients. The proposed solution explores the promising field of deep learning methods integrated with reinforcement learning schema, showing its benefits against other state of the art approaches. The great knowledge transfer capability of artificial intelligence methods and the benefits of representation learning systems stand out from this research, representing the common thread among all the presented research fields

Graph Neural Networks for Improved Interpretability and Efficiency

Attributed graph is a powerful tool to model real-life systems which exist in many domains such as social science, biology, e-commerce, etc. The behaviors of those systems are mostly defined by or dependent on their corresponding network structures. Graph analysis has become an important line of research due to the rapid integration of such systems into every aspect of human life and the profound impact they have on human behaviors. Graph structured data contains a rich amount of information from the network connectivity and the supplementary input features of nodes. Machine learning algorithms or traditional network science tools have limitation in their capability to make use of both network topology and node features. Graph Neural Networks (GNNs) provide an efficient framework combining both sources of information to produce accurate prediction for a wide range of tasks including node classification, link prediction, etc. The exponential growth of graph datasets drives the development of complex GNN models causing concerns about processing time and interpretability of the result. Another issue arises from the cost and limitation of collecting a large amount of annotated data for training deep learning GNN models. Apart from sampling issue, the existence of anomaly entities in the data might degrade the quality of the fitted models. In this dissertation, we propose novel techniques and strategies to overcome the above challenges. First, we present a flexible regularization scheme applied to the Simple Graph Convolution (SGC). The proposed framework inherits fast and efficient properties of SGC while rendering a sparse set of fitted parameter vectors, facilitating the identification of important input features. Next, we examine efficient procedures for collecting training samples and develop indicative measures as well as quantitative guidelines to assist practitioners in choosing the optimal sampling strategy to obtain data. We then improve upon an existing GNN model for the anomaly detection task. Our proposed framework achieves better accuracy and reliability. Lastly, we experiment with adapting the flexible regularization mechanism to link prediction task