Autonomic Clustering of Distributed Agents Free

Author: Elizabeth Ogston
Edition: 2012
Binding: Paperback
ISBN: 376439966X

Autonomic Clustering of Distributed Agents (Autonomic Systems)

A central principle in the design of large-scale distributed systems is that components should be organized to place those that interact frequently close together. Get Autonomic Clustering of Distributed Agents computer books for free.
This is essentially a basic clustering problem, but the context creates new challenges. Traditional clustering algorithms are designed to work on relatively simple units of information stored in a centralized database. This work explores the consequences of clustering autonomous entities, each with individual, possibly different, criteria defining similarity and cluster composition requirements. In this setting clustering is transformed from being mainly a catagorization task, into a problem of discovering similarity criteria and classification categories. Original research results Check Autonomic Clustering of Distributed Agents our best computer books for 2013. All books are available in pdf format and downloadable from rapidshare, 4shared, and mediafire.

Autonomic Clustering of Distributed Agents Free

This is essentially a basic clustering problem, but the context creates new challenges. Traditional clustering algorithms are designed to work on relatively simple units of information stored in a centralized database. This work explores the consequences of clustering autonomous entities, each with individual, possibly different, criteria defining similarity and cluster composition requirements. In this setting clustering is transformed from being mainly a catagorization task, into a problem of discovering similarity criteria and classification categories his is essentially a basic clustering problem, but the context creates new challenges. Traditional clustering algorithms are designed to work on relatively simple units of information stored in a centralized database. This work explores the consequences of clustering autonomous entities, each with individual, possibly different, criteria defining similarity and cluster composition requirements. In this setting clustering is transformed from being mainly a catagorization task, into a problem of discovering similarity criteria and classification categories. Original research results

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