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Advances in knowledge discovery in databases

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Advances in knowledge discovery in databases/ by Animesh Adhikari, Jhimli Adhikari.
Author:	Adhikari, Animesh.
other author:	Adhikari, Jhimli.
Published:	Cham :Springer International Publishing : : 2015.,
Description:	xv, 370 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Data mining. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-13212-9
ISBN:	9783319132129 (electronic bk.)

Advances in knowledge discovery in databases
Adhikari, Animesh.

Advances in knowledge discovery in databases[electronic resource] /by Animesh Adhikari, Jhimli Adhikari. - Cham :Springer International Publishing :2015. - xv, 370 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.791868-4394 ;. - Intelligent systems reference library ;v. 3..

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.

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.

ISBN: 9783319132129 (electronic bk.)

Standard No.: 10.1007/978-3-319-13212-9doiSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Advances in knowledge discovery in databases
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505 0 $a 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.
520 $a 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.
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