Edizione	[1st ed.]
Pubbl/distr/stampa	Austin, TX, : IBM, International Technical Support Organization, 2005
Descrizione fisica	xii, 252 p. : ill
Disciplina	005.741
Collana	Redbooks
Soggetto topico	Parallel processing (Electronic computers) File organization (Computer science) Computer network architectures Digital media
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Record Nr.	UNINA-9910824917303321

Edizione	[1st edition]
Pubbl/distr/stampa	Boca Raton, FL : , : Chapman and Hall/CRC, , [2018]
Descrizione fisica	1 online resource (648 p.)
Disciplina	519.535
Collana	Chapman & Hall/CRC data mining and knowledge discovery series
Soggetto topico	Cluster analysis Data mining Document clustering File organization (Computer science) Machine theory
Soggetto genere / forma	Electronic books.
ISBN	1-315-36278-3 1-315-37351-3 1-4665-5821-0
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Front Cover; Contents; Preface; Editor Biographies; Contributors; Chapter 1: An Introduction to Cluster Analysis; Chapter 2: Feature Selection for Clustering: A Review; Chapter 3: Probabilistic Models for Clustering; Chapter 4: A Survey of Partitional and Hierarchical Clustering Algorithms; Chapter 5: Density-Based Clustering; Chapter 6: Grid-Based Clustering; Chapter 7: Nonnegative Matrix Factorizations for Clustering: A Survey; Chapter 8: Spectral Clustering; Chapter 9: Clustering High-Dimensional Data; Chapter 10: A Survey of Stream Clustering Algorithms; Chapter 11: Big Data Clustering Chapter 12: Clustering Categorical DataChapter 13: Document Clustering: The Next Frontier; Chapter 14 : Clustering Multimedia Data; Chapter 15: Time-Series Data Clustering; Chapter 16: Clustering Biological Data; Chapter 17: Network Clustering; Chapter 18: A Survey of Uncertain Data Clustering Algorithms; Chapter 19: Concepts of Visual and Interactive Clustering; Chapter 20: Semisupervised Clustering; Chapter 21: Alternative Clustering Analysis: A Review; Chapter 22 : Cluster Ensembles: Theory and Applications; Chapter 23: Clustering ValidationMeasures Chapter 24: Educational and Software Resources for DataClusteringColor Inserts; Back Cover
Record Nr.	UNINA-9910463459403321

Autore	Street, C.A.
Edizione	[1 ed.]
Pubbl/distr/stampa	Maidenhead : McGraw-Hill, c1984
Descrizione fisica	130 p. ; 24 cm.
Altri autori (Persone)	Zappalà, Giuseppe
Soggetto topico	Basic (Computer program language) Database management File organization (Computer science) Sinclair ZX Spectrum (Computer) - Programma
ISBN	8877000023
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Record Nr.	UNISALENTO-991001577919707536

Autore	Turkington Garry
Edizione	[1st edition]
Pubbl/distr/stampa	Birmingham, : Packt Publishing, 2013
Descrizione fisica	1 online resource (398 p.)
Disciplina	005.74
Soggetto topico	Apache Hadoop (Computer file) Database management Electronic data processing -- Distributed processing File organization (Computer science) Hadoop (Computer program)
Soggetto genere / forma	Electronic books.
ISBN	1-5231-3215-9 1-68015-106-1 1-299-26161-2 1-84951-731-2
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: What It's All About; Big data processing; The value of data; Historically for the few and not the many; Classic data processing systems; Limiting factors; A different approach; All roads lead to scale-out; Share nothing; Expect failure; Smart software, dumb hardware; Move processing, not data; Build applications, not infrastructure; Hadoop; Thanks Google; Thanks Doug; Thanks Yahoo; Parts of Hadoop; Common building blocks; HDFS; MapReduce; Better together Common architectureWhat it is and isn't good for; Cloud computing with Amazon Web Services; Too many clouds; A third way; Different types of cost; AWS - infrastructure on demand from Amazon; Elastic Compute Cloud (EC2); Simple Storage Service (S3); Elastic MapReduce (EMR); What this book covers; A dual approach; Summary; Chapter 2: Getting Hadoop Up and Running; Hadoop on a local Ubuntu host; Other operating systems; Time for action - checking the prerequisites; Setting up Hadoop; A note on versions; Time for action - downloading Hadoop; Time for action - setting up SSH Configuring and running HadoopTime for action - using Hadoop to calculate Pi; Three modes; Time for action - configuring the pseudo-distributed mode; Configuring the base directory and formatting the filesystem; Time for action - changing the base HDFS directory; Time for action - formatting the NameNode; Starting and using Hadoop; Time for action - starting Hadoop; Time for action - using HDFS; Time for action - WordCount, the Hello World of MapReduce; Monitoring Hadoop from the browser; The HDFS web UI; Using Elastic MapReduce; Setting up an account on Amazon Web Services Creating an AWS accountSigning up for the necessary services; Time for action - WordCount on EMR using the management console; Other ways of using EMR; AWS credentials; The EMR command-line tools; The AWS ecosystem; Comparison of local versus EMR Hadoop; Summary; Chapter 3: Understanding MapReduce; Key/value pairs; What does it mean; Why key/value data?; Some real-world examples; MapReduce as a series of key/value transformations; The Hadoop Java API for MapReduce; The 0.20 MapReduce Java API; The Mapper class; The Reducer class; The Driver class; Writing MapReduce programs Time for action - setting up the classpathTime for action - implementing WordCount; Time for action - building a JAR file; Time for action - running WordCount on a local Hadoop cluster; Time for action - running WordCount on EMR; The pre-0.20 Java MapReduce API; Hadoop-provided mapper and reducer implementations; Time for action - WordCount the easy way; Walking through a run of WordCount; Startup; Splitting the input; Task assignment; Task startup; Ongoing JobTracker monitoring; Mapper input; Mapper execution; Mapper output and reduce input; Partitioning; The optional partition function Reducer input
Record Nr.	UNINA-9910465656103321