Continuous and incremental data mining association rules using frame metadata model

Abstract

Most organizations have large databases that contain a wealth of potentially accessible information. The unlimited growth of data will inevitably lead to a situation in which it is increasingly difficult to access the desired information. There is a need to extract knowledge from data by knowledge discovery in database (KDD). Data mining is the discovery stage of KDD whereas association rule is a possible product. It states a statistical correlation between the occurrence of certain attributes in a database table. Such correlation is continuously changing subject to the new updates in the source database. Data mining association rules are often done by computing the association rules for the whole source database. In this paper, we introduce a frame metadata model to facilitate the continuous association rules generation in data mining. A new set of association rules can be derived with the update of the source databases by the data operation function in the frame metadata model. The frame metadata model consists of two types of classes: static classes and active classes. The active classes are event driven, obtaining data from the database when invoked by a certain event. The static classes describe data of the association rule table. Whenever an update occurs in the existing base relations, a corresponding update will be invoked by an event attribute in the method class which will compute the association rules continuously. The result is an active data mining capable of deriving association rules of a source database continuously or incrementally using frame metadata model.