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OverviewData mining is a process to extract useful knowledge from large amounts of data. To conduct data mining, we often need to collect data. However, privacy concerns may prevent people from sharing the data and some types of information about the data. How we conduct data mining without breaching data privacy presents a challenge. Secure Data Mining provides solutions to the problem of data mining without compromising data privacy. This professional book is designed for practitioners and researchers in industry, as well as a secondary textbook for advanced-level students in computer science. Full Product DetailsAuthor: Justin Zhan , Stan MatwinPublisher: Springer-Verlag New York Inc. Imprint: Springer-Verlag New York Inc. Edition: 1st ed. 2024 ISBN: 9780387879659ISBN 10: 038787965 Pages: 280 Publication Date: 28 August 2024 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: Awaiting stock  The supplier is currently out of stock of this item. It will be ordered for you and placed on backorder. Once it does come back in stock, we will ship it out for you. Table of ContentsPreface.- Introduction.- Literature Review.- Fundamental Security and Privacy.- Privacy-Preserving Association Rule Mining.- Privacy-Preserving Sequential Pattern Mining.- Privacy-Preserving Naive Bayesian Classification.- Privacy-Preserving Decision Tree Classification.- Privacy-Preserving k-Nearest Neighbor Classification.- Privacy-Preserving Support Vector Machine Classification.- Privacy-Preserving k-Mean Clustering.- Privacy-Preserving k-Medoids Clustering.- Other Selected Topics.- Conclusion and Future Work.- Index.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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