Abstract
A number of important decisions are based on a set of specific items in a database called select items. Thus the analysis of select items in multiple databases becomes 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 algorithm that is based on the proposed overall association between two items in a database for the purpose of grouping the frequent items in multiple databases. Each group contains a select item called the nucleus item and the group grows while being centered around the nucleus item. Experimental results are concerned with some synthetic and real-world databases.
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Adhikari, A., Adhikari, J., Pedrycz, W. (2014). Mining Patterns of Select Items in Different Data Sources. In: Data Analysis and Pattern Recognition in Multiple Databases. Intelligent Systems Reference Library, vol 61. Springer, Cham. https://doi.org/10.1007/978-3-319-03410-2_6
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DOI: https://doi.org/10.1007/978-3-319-03410-2_6
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