WIT Press

Association Rule Descriptive Language


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WIT Press


Z Hossain & Sk Ahad Ali


Mining association rules in large databases is one of the most interesting data mining techniques in database communities. The number of association rules in a large database highly depends on rule’s support and confidence, and there might exist hundreds of rules in a very large database. The presentation of large number of rules in a very nice and noticeable way becomes highly challenging. In recent years researchers have developed several tools to visualize association rules. However, a large number of the tools cannot handle more than dozens of association rules. Furthermore, none of them can effectively manage association rules with multiple antecedents. Till now a uniform descriptive presentation technique has not been set up yet. We studied existing descriptive techniques in the context of visualization and introduced a graph-based technique as Unified Descriptive Language for Association Rules. The proposed technique can be used to extract the discovered rules of a very large database very conveniently and efficiently. 1 Introduction The discovery of hidden but essential information from large databases is known as data mining. Data mining enables us to find useful and invaluable information from large databases. Data mining is also known as knowledge discovery in databases, knowledge extraction, data archeology, data dredging, knowledge mining, data analysis, etc [24]. Depending upon the application, the extractable knowledge in a database is categorized as mining association rules, data generalization, data classification, and data clustering. The association rule mining, discovering certain associations among a set of objects within a database, is one of the most interesting techniques but most important problems