Supervised and Unsupervised Learning for Data Science

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along wit

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Michael W. Berry Azlinah Mohamed Bee Wah Yap Editors

Supervised and Unsupervised Learning for Data Science

Unsupervised and Semi-Supervised Learning Series Editor M. Emre Celebi, Computer Science Department, Conway, Arkansas, USA

Springer’s Unsupervised and Semi-Supervised Learning book series covers the latest theoretical and practical developments in unsupervised and semi-supervised learning. Titles – including monographs, contributed works, professional books, and textbooks – tackle various issues surrounding the proliferation of massive amounts of unlabeled data in many application domains and how unsupervised learning algorithms can automatically discover interesting and useful patterns in such data. The books discuss how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. Books also discuss semi-supervised algorithms, which can make use of both labeled and unlabeled data and can be useful in application domains where unlabeled data is abundant, yet it is possible to obtain a small amount of labeled data. Topics of interest in include: − − − − − − − − − −

Unsupervised/Semi-Supervised Discretization Unsupervised/Semi-Supervised Feature Extraction Unsupervised/Semi-Supervised Feature Selection Association Rule Learning Semi-Supervised Classification Semi-Supervised Regression Unsupervised/Semi-Supervised Clustering Unsupervised/Semi-Supervised Anomaly/Novelty/Outlier Detection Evaluation of Unsupervised/Semi-Supervised Learning Algorithms Applications of Unsupervised/Semi-Supervised Learning

While the series focuses on unsupervised and semi-supervised learning, outstanding contributions in the field of supervised learning will also be considered. The intended audience includes students, researchers, and practitioners.

More information about this series at http://www.springer.com/series/15892

Michael W. Berry • Azlinah Mohamed Bee Wah Yap Editors

Supervised and Unsupervised Learning for Data Science

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Editors Michael W. Berry Department of Electrical Engineering and Computer Science University of Tennessee at Knoxville Knoxville, TN, USA

Azlinah Mohamed Faculty of Computer & Mathematical Sciences Universiti Teknologi MARA Shah Alam, Selangor, Malaysia

Bee Wah Yap Advanced Analytics Engineering Centre, Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Shah Alam, Selangor, Malaysia

ISSN 2522-848X ISSN 2522-8498 (electronic) Unsupervised and Semi-Supervised Learning ISBN 978-3-030-22474-5 ISBN 978-3-030-22475-2 (eBook) https://doi.org/10.1007/978-3-030-22475-2 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, an