Abstract
Methods of clustering for categorical and mixed data are considered. Dissimilarities for this purpose are reviewed and different classes of algorithms according to different classes of similarities are discussed. Details of several algorithms are then given, which include agglomerative hierarchical clustering, K-means and related methods such as K-medoids and K-modes, and methods of network clustering. The way how the combinations of existing ideas leads to new algorithms is discussed.
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Acknowledgment
This paper is based upon work supported in part by the Air Force Office of Scientific Research/Asian Office of Aerospace Research and Development (AFOSR/AOARD) under award number FA2386-17-1-4046.
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Miyamoto, S., Huynh, VN., Fujiwara, S. (2018). Methods for Clustering Categorical and Mixed Data: An Overview and New Algorithms. In: Huynh, VN., Inuiguchi, M., Tran, D., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2018. Lecture Notes in Computer Science(), vol 10758. Springer, Cham. https://doi.org/10.1007/978-3-319-75429-1_7
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