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

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J. A. L. Santos,M. Mateus,J. M. S. Cabral low quality from multiple databases, it becomes necessary to improve mining multiple databases. In this chapter, we present an idea of multi-database mining by making use of local pattern analysis. We elaborate on the existing specialized and generalized techniques which are used for mining multipl

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Trennung und Charakterisierung von Proteinenecomes of primordial relevance. In this chapter, we focus on the following issues. First, a model of mining global patterns of select items from multiple databases is presented. Second, a measure of quantifying an overall association between two items in a database is discussed. Third, we present an

BINGE

https://doi.org/10.1007/978-3-662-29253-2the number of branches of a multi-branch company is increasing over time. Thus, it is important and timely to study data mining carried out on multiple data sources. A global exceptional pattern describes interesting individuality and specificity of few branches. Therefore, it is interesting to iden

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https://doi.org/10.1007/978-1-4684-8062-7er growth of the organizations. For instance, it might be of interest to learn about interesting changes in sales over time. In this chapter, we introduce a new pattern, called notch, of an item in time-stamped databases. Based on this notion, we propose a special kind of notch, called a generalized

AFFIX

S. J. Murphy,E. D. Morgan,I. D. Wilsonant task. Recently, researchers have reported an algorithm for finding calendar-based periodic pattern in a time-stamped data and introduced the concept of certainty factor in association with an overlapped interval. In this chapter, we have extended the concept of certainty factor by incorporating

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P. Leif Bergsagel,W. Michael Kuehlresting as well as challenging when we are required to identify patterns and associations in multiple large data sources. While dealing with the domain of multiple large data sources, it has been observed that many patterns are specific to this domain; also some patterns are extensions of classical

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Data Analysis and Pattern Recognition in Multiple Databases978-3-319-03410-2Series ISSN 1868-4394 Series E-ISSN 1868-4408

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J. A. L. Santos,M. Mateus,J. M. S. Cabral low quality from multiple databases, it becomes necessary to improve mining multiple databases. In this chapter, we present an idea of multi-database mining by making use of local pattern analysis. We elaborate on the existing specialized and generalized techniques which are used for mining multiple large databases.