Querying And Mining Uncertain Data Streams (Paperback)

Aoying Zhou

ISBN 10: 9813108789 / ISBN 13: 9789813108783

Published by World Scientific Publishing Co Pte Ltd, Singapore, 2016

About this Item

Paperback. Data uncertainty widely exists in many applications, and an uncertain data stream is a series of uncertain tuples that arrive rapidly. However, traditional techniques for deterministic data streams cannot be applied to deal with data uncertainty directly due to the exponential growth of possible solution space.This book provides a comprehensive overview of the authors' work on querying and mining uncertain data streams. Its contents include some important discoveries dealing with typical topics such as top-k query, ER-Topk query, rarity estimation, set similarity, and clustering.Querying and Mining Uncertain Data Streams is written for professionals, researchers, and graduate students in data mining and its various related fields. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9789813108783

Bibliographic Details

• Title: Querying And Mining Uncertain Data Streams (Paperback)
• Publisher: World Scientific Publishing Co Pte Ltd, Singapore
• Publication Date: 2016
• Language: English
• ISBN 10: 9813108789
• ISBN 13: 9789813108783
• Binding: Paperback
• Condition: new

About this title

Synopsis

Data uncertainty widely exists in many applications, and an uncertain data stream is a series of uncertain tuples that arrive rapidly. However, traditional techniques for deterministic data streams cannot be applied to deal with data uncertainty directly due to the exponential growth of possible solution space. This book provides a comprehensive overview of the authors' work on querying and mining uncertain data streams. Its contents include some important discoveries dealing with typical topics such as top-k query, Er-Topk query, rarity estimation, set similarity, and clustering. Querying and Mining Uncertain Data Streams is written for professionals, researchers, and graduate students in data mining and its various related fields.