Abstract
This paper presents a new rule-based classification method that partitions data under analysis into spherical patterns. The forte of the method is twofold. One, it exploits the efficiency of distance metric-based clustering to fast collect similar data into spherical patterns. The other, spherical patterns are each a trait shared among one type of data only, hence are built for classification of new data. Numerical studies with public machine learning datasets from Lichman (2013), in comparison with well-established classification methods from Boros et al. (IEEE Transactions on Knowledge and Data Engineering, 12, 292–306, 2000) and Waikato Environment for Knowledge Analysis ( http://www.cs.waikato.ac.nz/ml/weka/ ), demonstrate the aforementioned utilities of the new method well.
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