Algorithms for clustering data
Algorithms for clustering data
A new version of the rule induction system LERS
Fundamenta Informaticae
A New Version of Rough Set Exploration System
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Automatic Information Organization and Retrieval.
Automatic Information Organization and Retrieval.
Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining
Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining
Data Mining: A Knowledge Discovery Approach
Data Mining: A Knowledge Discovery Approach
Cluster Analysis for Data Mining and System Identification
Cluster Analysis for Data Mining and System Identification
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Authors propose a new approach in the optimization of inference processes in decision support systems with incomplete knowledge. The idea is based on clustering large set of rules from knowledge bases as long as it is necessary to find a relevant rule as quickly as possible. This work is highly focused on the results of experiments regarding the influence of Agnes' algorithm parameters on the quality of the clustering process. Additionally, the authors present the results of the experiments regarding the optimal amount of groups formed by decision rules.