Mining Several Data Bases with an Ensemble of Classifiers

The results of knowledge discovery in databases could vary depending on the data mining method. There are several ways to select the most appropriate data mining method dynamically. One proposed method clusters the whole domain area into competence areas of the methods. A metamethod is then used to decide which data mining method should be used with each database instance. However, when knowledge is extracted from several databases knowledge discovery may produce conflicting results even if the separate databases are consistent. At least two types of conflicts may arise. The first type is created by data inconsistency within the area of the intersection of the databases. The second type of conflicts is created when the metamethod selects different data mining methods with inconsistent competence maps for the objects of the intersected part. We analyze these two types of conflicts and their combinations and suggest ways to handle them.

• Type: Conference
• Year: 1999
• Where: DEXAW
• Authors: Seppo Puuronen, Vagan Y. Terziyan, Alexander Logvinovsky
• Keywords: Appropriate Data Mining, Data Mining Methods, Database, DEXAW 1999, Knowledge Discovery