Data Analysis and Pattern Recognition in Multiple Databases (Record no. 54679)

000 -LEADER
fixed length control field 03635nam a22005175i 4500
001 - CONTROL NUMBER
control field 978-3-319-03410-2
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200421111656.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 131206s2014 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319034102
-- 978-3-319-03410-2
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author Adhikari, Animesh.
245 10 - TITLE STATEMENT
Title Data Analysis and Pattern Recognition in Multiple Databases
300 ## - PHYSICAL DESCRIPTION
Number of Pages XV, 238 p. 97 illus.
490 1# - SERIES STATEMENT
Series statement Intelligent Systems Reference Library,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 From the Contents: Synthesizing Different Extreme Association Rules in Multiple Data Sources -- Clustering items in time-stamped databases induced by stability -- Mining global patterns in multiple large databases -- Clustering Local Frequency Items in Multiple Data Sources -- Mining Patterns of Select Items in Different Data Sources.
520 ## - SUMMARY, ETC.
Summary, etc Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyse them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery, and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.
700 1# - AUTHOR 2
Author 2 Adhikari, Jhimli.
700 1# - AUTHOR 2
Author 2 Pedrycz, Witold.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-319-03410-2
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2014.
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-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
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-- online resource
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-- rdacarrier
347 ## -
-- text file
-- PDF
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data mining.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Pattern recognition.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Pattern Recognition.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Mining and Knowledge Discovery.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 1868-4394 ;
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-- ZDB-2-ENG

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