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OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20140310151113.0 |
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131113s2013 xxk| s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781447156079 |
|
978-1-4471-5607-9 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
Q337.5 |
|
|---|
| Classification number |
TK7882.P3 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.4 |
| Edition number |
23 |
| 264 #1 - |
|---|
| -- |
London : |
| -- |
Springer London : |
| -- |
Imprint: Springer, |
| -- |
2013. |
| 912 ## - |
|---|
| -- |
ZDB-2-SCS |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ravindra Babu, T. |
| Relator term |
author. |
| 245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE |
|---|
| Title |
Compression Schemes for Mining Large Datasets |
| Medium |
[electronic resource] : |
| Remainder of title |
A Machine Learning Perspective / |
| Statement of responsibility, etc |
by T. Ravindra Babu, M. Narasimha Murty, S.V. Subrahmanya. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
XVI, 197 p. 62 illus., 3 illus. in color. |
| Other physical details |
online resource. |
| 440 1# - SERIES STATEMENT/ADDED ENTRY--TITLE |
|---|
| Title |
Advances in Computer Vision and Pattern Recognition, |
| International Standard Serial Number |
2191-6586 |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Introduction -- Data Mining Paradigms -- Run-Length Encoded Compression Scheme -- Dimensionality Reduction by Subsequence Pruning -- Data Compaction through Simultaneous Selection of Prototypes and Features -- Domain Knowledge-Based Compaction -- Optimal Dimensionality Reduction -- Big Data Abstraction through Multiagent Systems -- Intrusion Detection Dataset: Binary Representation. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
As data mining algorithms are typically applied to sizable volumes of high-dimensional data, these can result in large storage requirements and inefficient computation times. This unique text/reference addresses the challenges of data abstraction generation using a least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain. Schemes are presented which are shown to be efficient both in terms of space and time, while simultaneously providing the same or better classification accuracy, as illustrated using high-dimensional handwritten digit data and a large intrusion detection dataset. Topics and features: Presents a concise introduction to data mining paradigms, data compression, and mining compressed data Describes a non-lossy compression scheme based on run-length encoding of patterns with binary valued features Proposes a lossy compression scheme that recognizes a pattern as a sequence of features and identifying subsequences Examines whether the identification of prototypes and features can be achieved simultaneously through lossy compression and efficient clustering Discusses ways to make use of domain knowledge in generating abstraction Reviews optimal prototype selection using genetic algorithms Suggests possible ways of dealing with big data problems using multiagent systems A must-read for all researchers involved in data mining and big data, the book proposes each algorithm within a discussion of the wider context, implementation details and experimental results. These are further supported by bibliographic notes and a glossary. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer science. |
|
|---|
| Topical term or geographic name as entry element |
Data mining. |
|
|---|
| Topical term or geographic name as entry element |
Artificial intelligence. |
|
|---|
| Topical term or geographic name as entry element |
Optical pattern recognition. |
|
|---|
| Topical term or geographic name as entry element |
Computer Science. |
|
|---|
| Topical term or geographic name as entry element |
Pattern Recognition. |
|
|---|
| Topical term or geographic name as entry element |
Data Mining and Knowledge Discovery. |
|
|---|
| Topical term or geographic name as entry element |
Artificial Intelligence (incl. Robotics). |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Narasimha Murty, M. |
| Relator term |
author. |
|
|---|
| Personal name |
Subrahmanya, S.V. |
| Relator term |
author. |
| 710 2# - ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY |
|---|
| Title |
Springer eBooks |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Display text |
Printed edition: |
| International Standard Book Number |
9781447156062 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
http://dx.doi.org/10.1007/978-1-4471-5607-9 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
E-Book |
