Abstract
Particle classification is one of the major analyses in high-energy particle physics experiments. We design a classification framework combining classification and clustering for particle physics experiments data. The system involves classification by a set of Artificial Neural Networks (ANN); each using distinct subsets of samples selected from the general set. We use frequent variable sets based clustering for partitioning the train samples into several natural subsets, then standard back-propagation ANNs are trained on them. The final decision for each test case is a two-step process. First, the nearest cluster is found for the case, and then the decision is based on the ANN classifier trained on the specific cluster. Comparisons with other classification and clustering methods show that our method is promising.
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Jin, X., Bie, R. (2007). Frequent Variable Sets Based Clustering for Artificial Neural Networks Particle Classification. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds) Advances in Data and Web Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72524-4_88
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DOI: https://doi.org/10.1007/978-3-540-72524-4_88
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