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The non-supervised classification algorithms determine clusters such that objects in the same cluster are similar among them, while objects in different clusters are less similar. However, there are some practical problems where, besides determining the clusters, the properties that characterize them are required. This problem is known as conceptual clustering. There are different methods that allow to solve the conceptual clustering problem, one of them is the conceptual k-means algorithm, which is a conceptual version of the k-means algorithm; one of the most studied and used algorithms for solving the restricted non-supervised classification problem (when the number of clusters is specified a priori). The main characteristic of the conceptual k-means algorithm is that it requires generalization lattices for the construction of the concepts. In this thesis, an improvement of the conceptual k-means algorithm and a new conceptual k-means algorithm that does not depend on generalization lattices for building the concepts are proposed. Finally, in this thesis, two fuzzy conceptual clustering algorithms, which are fuzzy versions of the proposed hard conceptual clustering algorithms, are introduced

Palabras clave: Conceptual Clustering, Fuzzy Conceptual Clustering, Similarity Functions, Mixed Data.
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Universidad Autónoma del Estado de México
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Versión 3.0 | 2018
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