A Simple Cluster Validation Index with Maximal Coverage
Jauhiainen, S., & Kärkkäinen, T. (2017). A Simple Cluster Validation Index with Maximal Coverage. In ESANN 2017 : Proceedings of the 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 293-298). ESANN. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2017-24.pdf
© the Authors, 2017.
Clustering is an unsupervised technique to detect general, distinct profiles from a given dataset. Similarly to the existence of various different clustering methods and algorithms, there exists many cluster validation methods and indices to suggest the number of clusters. The purpose of this paper is, firstly, to propose a new, simple internal cluster validation index. The index has a maximal coverage: also one cluster, i.e., lack of division of a dataset into disjoint subsets, can be detected. Secondly, the proposed index is compared to the available indices from five different packages implemented in R or Matlab to assess its utilizability. The comparison also suggests many interesting findings in the available implementations of the existing indices. The experiments and the comparison support the viability of the proposed cluster validation index.
Parent publication ISBN978-2-87587-039-1
ConferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Is part of publicationESANN 2017 : Proceedings of the 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
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