Efficient Classification from Multiple Heterogeneous Databases

Abstract

Abstract. With the fast expansion of computer networks, it is inevitable to study data mining on heterogeneous databases. In this paper we propose MDBM, an accurate and efficient approach for classification on multiple heterogeneous databases. We propose a regression-based method for predicting the usefulness of inter-database links that serve as bridges for information transfer, because such links are automatically detected and may or may not be useful or even valid. Because of the high cost of inter-database communication, MDBM employs a new strategy for cross-database classification, which finds and performs actions with high benefit-to-cost ratios. The experiments show that MDBM achieves high accuracy in cross-database classification, with much higher efficiency than previous approaches.

A review on comparative performance analysis of associative classifiers

“…The associative classification approach CMAR possesses capabilities to mines large database efficiently. Yin and Han (2005) proposed a new classification approach which combines the advantages of both associative classification and traditional rule-based classification namely known as Classification based on Predictive Association Rules (CPAR). The CPAR exploits a greedy algorithm for the generation of rules directly from the training data.…”

Integration of useful links in distributed databases using decision tree classification

“…Yin and Han [13] developed a rule-based classification approach by introducing the notion of link utility. Their algorithm named MDBM is used mainly for the classification in multiple and heterogeneous databases.…”

“…that permits to have the lowest communication cost. A technique inspired from [13] consists of choosing the attribute having the lowest ratio. This technique is defined as follows.…”

Synthesizing heavy association rules from different real data sources

“…Wu et al (2005) have proposed a database clustering technique for multi-database mining. Yin and Han (2005) have proposed a new strategy for relational heterogeneous database classification. Zhang and Zaki (2006) have edited a book on multi-database mining.…”