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.

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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

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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.