Modern Algorithms of Cluster Analysis: 34 (Studies in Big Data, 34) - Image 1

£130.80

Springer Modern Algorithms of Cluster Analysis: 34 (Studies in Big Data, 34)

Name: Modern Algorithms of Cluster Analysis: 34 (Studies in Big Data, 34)
Brand: Springer
Price: 130.8 GBP
Availability: InStock

AI & Machine Learning

About as cheap as it gets. The only time it was cheaper was 1 year ago.

£131 today · all-time low £126 (Feb 2025) · usually the usual

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 637 days • 637 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 637 days • 6 price levels

Days at Price

Current Price

Price Analysis

Most common price: £126 (360 days, 56.5%)

Price range: £126 - £150

Price levels: 6 different prices over 637 days

Description

This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem. Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented. In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection. From the Back Cover This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem. Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented. In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection.

Product Specifications

Brand: Springer
Format: Paperback
ASIN: 3319887521
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 04 June 2019
Listed Since: 04 January 2019

Barcode

No barcode data available

Springer Modern Algorithms of Cluster Analysis: 34 (Studies in Big Data, 34)

About as cheap as it gets. The only time it was cheaper was 1 year ago.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing (Springer Tracts in Nature-Inspired Computing)

Reverse Clustering: Formulation, Interpretation and Case Studies: 957 (Studies in Computational Intelligence, 957)

Next Generation Data Technologies for Collective Computational Intelligence: 352 (Studies in Computational Intelligence, 352)

Granular Neural Networks, Pattern Recognition and Bioinformatics: 712 (Studies in Computational Intelligence, 712)

Granular Neural Networks, Pattern Recognition and Bioinformatics: 712 (Studies in Computational Intelligence, 712)

MDATA: A New Knowledge Representation Model: Theory, Methods and Applications: 12647 (Lecture Notes in Computer Science, 12647)

From Security to Community Detection in Social Networking Platforms (Lecture Notes in Social Networks)

Advances in Internet, Data and Web Technologies: The 9th International Conference on Emerging Internet, Data & Web Technologies (EIDWT-2021): 65 ... and Communications Technologies, 65)

Intelligent Systems in Big Data, Semantic Web and Machine Learning: 1344 (Advances in Intelligent Systems and Computing, 1344)

Advances in Internet, Data & Web Technologies: The 10th International Conference on Emerging Internet, Data and Web Technologies (EIDWT-2022): 118 ... and Communications Technologies, 118)

Recent Advances on Soft Computing and Data Mining: The Second International Conference on Soft Computing and Data Mining (SCDM-2016), Bandung, ... in Intelligent Systems and Computing, 549)

Nature-Inspired Computation in Data Mining and Machine Learning: 855 (Studies in Computational Intelligence, 855)

Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective (Data-Enabled Engineering)

Soft Computing in Big Data Processing: 271 (Advances in Intelligent Systems and Computing, 271)

Granular Computing Based Machine Learning: A Big Data Processing Approach: 35 (Studies in Big Data, 35)

Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing (Springer Tracts in Nature-Inspired Computing)

Towards Digital Intelligence Society: A Knowledge-based Approach: 1281 (Advances in Intelligent Systems and Computing, 1281)

Quality Issues in the Management of Web Information: 50 (Intelligent Systems Reference Library, 50)

Handbook of Computational Social Science - Vol 1 & Vol 2: 1-2 (European Association of Methodology Series)

Intelligent Methods and Big Data in Industrial Applications: 40 (Studies in Big Data, 40)

Big Data Analytics: A Management Perspective: 21 (Studies in Big Data, 21)

Big Data and Smart Digital Environment: 53 (Studies in Big Data, 53)

ICT Analysis and Applications: Proceedings of ICT4SD 2020, Volume 2: 154 (Lecture Notes in Networks and Systems, 154)

Handbook of Research on Deep Learning Innovations and Trends (Advances in Computational Intelligence and Robotics)