Distributed data mining for synthesizing high frequency association rules: A Case study for determining service quality in hospitals

Abstract

Many large organizations have multiple data sources, while putting all data together from different sources might amass a huge database for centralized processing. Data mining involves the exploration and analysis of large amounts of data in order to discover meaning patterns. Data mining association rules at different data sources and forwarding the rules to the centralized company headquarter provides a feasible way to deal with multiple data source problems. However, the forwarded rules from different data sources may be too many for the centralized company headquarter to use. Therefore, there is a need to find high frequency rules that have major role in decision making process. A weighting method is proposed in this paper for identifying valid rules among the large number of forwarded rules from different data sources. Valid rules are the rules which are supported by most of the branches of an organization. Hence this method is applied to rank the rules based on patient perceived service qualities in a hospital. Experimental results show that this proposed weighting model is efficient and effective. Keywords: Association based data mining , Data reduction, weights, SERVQUAL scale.

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