Isbn: 9781108476348 - mining of massive datasets (14 Ergebnisse)

Zustand: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.

Hardcover. Zustand: Very Good. 3. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Hardcover. Zustand: New. 3. The item is brand new, never used or read. It's in perfect condition and may include supplements and/or access codes or come shrink-wrapped.

hardcover. Zustand: Very Good.

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In.

Hardcover. Zustand: Brand New. 3rd edition. 553 pages. 9.75x7.00x1.00 inches. In Stock.

Zustand: NEW.

Zustand: New. 2020. 3rd Edition. Hardcover. . . . . . Books ship from the US and Ireland.

Zustand: New. Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond. This third edition includes new and.

Hardcover. Zustand: Brand New. 3rd edition. 553 pages. 9.75x7.00x1.00 inches. In Stock.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 MapReduce 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 third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.…