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This book constitutes the thoroughly refereed post-conference proceedings of the Third International Conference on Advances in Data Science, ICIIT 2018, held in Chennai, India, in December 2018. The 11 full papers along with 4 short papers presented were carefully reviewed and selected from 74 submissions.The papers are organized in topical sections on data science foundations, data management and processing technologies, data analytics and its applications.

Data mining. --- Artificial intelligence. --- Computer science. --- Computer network architectures. --- Data Mining and Knowledge Discovery. --- Artificial Intelligence. --- Information Systems Applications (incl. Internet). --- Computer Applications. --- Computer Systems Organization and Communication Networks. --- Architectures, Computer network --- Network architectures, Computer --- Computer architecture --- Informatics --- Science --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Internet of things --- Artificial intelligence --- Computer network architectures --- IoT (Computer networks) --- Things, Internet of --- Computer networks --- Embedded Internet devices --- Machine-to-machine communications --- Application software. --- Computer organization. --- Organization, Computer --- Electronic digital computers --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software

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Computer science --- Computer architecture. Operating systems --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- datamining --- data science --- computers --- informatica --- computerbesturingssystemen --- informatiesystemen --- KI (kunstmatige intelligentie) --- OS (operating system) --- computernetwerken --- computerkunde --- data acquisition

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The US spends more than 4 trillion dollars per year on health care, largely conducted by private providers and reimbursed by insurers. A major concern in this system is overbilling, waste and fraud by providers, who face incentives to misreport on their claims in order to receive higher payments. In this work, we develop novel machine learning tools to identify providers that overbill insurers. Using large-scale claims data from Medicare, the US federal health insurance program for elderly adults and the disabled, we identify patterns consistent with fraud or overbilling among inpatient hospitalizations. Our proposed approach for fraud detection is fully unsupervised, not relying on any labeled training data, and is explainable to end users, providing reasoning and interpretable insights into the potentially suspicious behavior of the flagged providers. Data from the Department of Justice on providers facing anti-fraud lawsuits and case studies of suspicious providers validate our approach and findings. We also perform a post-analysis to understand hospital characteristics, those not used for detection but associate with a high suspiciousness score. Our method provides an 8-fold lift over random targeting, and can be used to guide investigations and auditing of suspicious providers for both public and private health insurance systems.