A conceptual version of the K-means algorithm
Pattern Recognition Letters
A compendium of machine learning: volume 1: symbolic machine learning
A compendium of machine learning: volume 1: symbolic machine learning
Extension to C-means Algorithm for the Use of Similarity Functions
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Acquisition of concept descriptions by conceptual clustering
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
RGC: A new conceptual clustering algorithm for mixed incomplete data sets
Mathematical and Computer Modelling: An International Journal
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The k-means algorithm is the most studied and used tool for solving the clustering problem when the number of clusters is known a priori. Nowadays, there is only one conceptual version of this algorithm, the conceptual k-means algorithm. One characteristic of this algorithm is the use of generalization lattices, which define relationships among the feature values. However, for many applications, it is difficult to determine the best generalization lattices; moreover there are not automatic methods to build the lattices, thus this task must be done by the specialist of the area in which we want to solve the problem. In addition, this algorithm does not work with missing data. For these reasons, in this paper, a new conceptual k-means algorithm that does not use generalization lattices to build the concepts and allows working with missing data is proposed. We use complex features for generating the concepts. The complex features are subsets of features with associated values that characterize objects of a cluster and at the same time not characterize objects from other clusters. Some experimental results obtained by our algorithm are shown and they are compared against the results obtained by the conceptual k-means algorithm.