Advances in knowledge discovery in databases [electronic resource] / Animesh Adhikari, Jhimli Adhikari.
Adhikari, Animesh, author.; Adhikari, Jhimli, author.
2015
QA76.9.D343
Available Online

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Title Advances in knowledge discovery in databases [electronic resource] / Animesh Adhikari, Jhimli Adhikari.
Author Adhikari, Animesh, author.
ISBN 9783319132129 electronic book
3319132121 electronic book
9783319132112
DOI https://doi.org/10.1007/978-3-319-13212-9
Published Cham : Springer, 2015.
Language English
Description 1 online resource (xv, 370 pages) : illustrations.
Item Number 10.1007/978-3-319-13212-9 doi
Call Number QA76.9.D343
Dewey Decimal Classification 006.3/12
Summary This book presents recent advances in Knowledge discovery in databases (KDD) with a focus on the areas of market basket database, time-stamped databases and multiple related databases. Various interesting and intelligent algorithms are reported on data mining tasks. A large number of association measures are presented, which play significant roles in decision support applications. This book presents, discusses and contrasts new developments in mining time-stamped data, time-based data analyses, the identification of temporal patterns, the mining of multiple related databases, as well as local patterns analysis.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed January 14, 2015).
Added Author Adhikari, Jhimli, author.
Series Intelligent systems reference library ; volume 79.
Available in Other Form Print version: 9783319132112
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Introduction
Synthesizing conditional patterns in a database
Synthesizing arbitrary Boolean expressions induced by frequent itemsets
Measuring association among items in a database
Mining association rules induced by item and quantity purchased
Mining patterns different related databases
Mining icebergs in different time-stamped data sources.-Synthesizing exceptional patterns in different data Sources
Clustering items in time-stamped databases
Synthesizing some extreme association rules from multiple databases
Clustering local frequency items in multiple data sources
Mining patterns of select items in different data sources
Mining calendar-based periodic patterns in time-stamped data
Measuring influence of an item in time-stamped databases
Clustering multiple databases induced by local patterns
Enhancing quality of patterns in multiple related databases
Concluding remarks.

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