Advances in Knowledge Discovery in Databases by Animesh AdhikariAdvances in Knowledge Discovery in Databases by Animesh Adhikari

Advances in Knowledge Discovery in Databases

byAnimesh Adhikari, Jhimli Adhikari

Hardcover | January 19, 2015

Pricing and Purchase Info

$205.29 online 
$248.50 list price save 17%
Earn 1,026 plum® points

Prices and offers may vary in store


In stock online

Ships free on orders over $25

Not available in stores


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.

Title:Advances in Knowledge Discovery in DatabasesFormat:HardcoverDimensions:370 pagesPublished:January 19, 2015Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3319132113

ISBN - 13:9783319132112

Look for similar items by category:


Table of Contents

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.