Mining Patterns of Select Items in Multiple Databases

Abstract

AbstractA number of important decisions are based on a set of specific items in a database called the 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.KeywordsFrequent ItemsetGlobal PatternFrequent ItemCentral OfficeLocal DatabaseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.