Mining of Massive Datasets, 2/e (Hardcover)

Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman

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商品描述

Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.

商品描述(中文翻譯)

由資料庫和網路技術的領先專家撰寫,這本書對學生和從業人員來說都是必讀之作。網路和網路商務的普及提供了許多極大的資料集,可以通過資料挖掘獲取信息。本書專注於實用的演算法,這些演算法已被用於解決資料挖掘中的關鍵問題,並且可以成功應用於最大的資料集。它首先討論了map-reduce框架,這是一個自動並行化演算法的重要工具。作者解釋了局部敏感哈希和流處理演算法的技巧,用於挖掘到達速度過快而無法進行全面處理的資料。其他章節涵蓋了PageRank概念以及用於組織網路的相關技巧,尋找頻繁項集和聚類的問題。這本第二版還包括了關於社交網路、機器學習和降維的新內容和擴展範圍。