Titlebook: Data Analysis and Pattern Recognition in Multiple Databases; Animesh Adhikari,Jhimli Adhikari,Witold Pedrycz Book 2014 Springer Internatio

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Introduction,Organizations that collect data from their multiple branches are common. Also, many established organizations possess data for a long period of time. Due to a spectrum of analyses, such data often need to be sub-divided into smaller databases.

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Synthesizing Different Extreme Association Rules from Multiple Databases,The model of local pattern analysis provides sound solutions to many multi-database mining problems. In this chapter we discuss different types of extreme association rules in multiple databases viz.

Amendment

A. E. Rodrigues,M. M. Dias,J. C. B. Lopesg clustering technique might cluster a set of items at a low level since it estimates association among items in an itemset with low accuracy, and thus a new algorithm for clustering local frequency items is proposed. Due to the suitability of measure of association . ., on its basis, association am

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Clustering Local Frequency Items in Multiple Data Sources,g clustering technique might cluster a set of items at a low level since it estimates association among items in an itemset with low accuracy, and thus a new algorithm for clustering local frequency items is proposed. Due to the suitability of measure of association . ., on its basis, association am

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1868-4394 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 patt978-3-319-37727-8978-3-319-03410-2Series ISSN 1868-4394 Series E-ISSN 1868-4408

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Book 2014 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 patt

conception

Mining Icebergs in Different Time-Stamped Data Sources, notch and subsequently, a specific type of generalized notch, called an iceberg, in time-stamped databases. We design an algorithm for mining interesting icebergs in time-stamped databases. We also present experimental results obtained for both synthetic and real-world databases.

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Measuring Influence of an Item in Time-Stamped Databases,hms of influence analysis involving specific items in a database. As the number of databases increases on a yearly basis, we have adopted incremental approach to these algorithms. Experimental results are reported for both synthetic and real-world databases.