On merging classification rules
One of the main challenges of today's data mining systems is their ability to manage a huge volume of data generated possibly by different sources. On the other hand, inductive learning algorithms have been extensively researched in machine learning using small amounts of judiciously chosen laboratory examples. There is an increasing concern in classifiers handling data that are substantially larger than available main memory on a single processor. One approach to the problem is to combine the results of different classifiers supplied with different subsets of the data, in parallel. In this paper, we present an efficient algorithm for combining partial classification rules. Moreover, the proposed algorithm can be used to match classification rules in a distributed environment, where different subsets of data may have different domains. The latter is achieved by using given concept hierarchies for the identification of matching classification rules. We also present empirical tests that demonstrate that the proposed algorithm has a significant speedup with respect to the analog non-distributed classification algorithm, at a cost of a lower classification accuracy.
classification data mining meta-learning