Institute of Information Theories and Applications FOI ITHEA
Abstract:
Today, due to globalization of the world the size of data set is increasing, it is necessary to discover the
knowledge. The discovery of knowledge can be typically in the form of association rules, classification rules,
clustering, discovery of frequent episodes and deviation detection. Fast and accurate classifiers for large
databases are an important task in data mining. There is growing evidence that integrating classification and
association rules mining, classification approaches based on heuristic, greedy search like decision tree induction.
Emerging associative classification algorithms have shown good promises on producing accurate classifiers. In
this paper we focus on performance of associative classification and present a parallel model for classifier
building. For classifier building some parallel-distributed algorithms have been proposed for decision tree
induction but so far no such work has been reported for associative classification.
