In this paper, we show how one can take advantage of the stability and effectiveness of object data clusteringalgorithms when the data to be clustered are available in the form Of Mutual numerical relationships betwe...
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In this paper, we show how one can take advantage of the stability and effectiveness of object data clusteringalgorithms when the data to be clustered are available in the form Of Mutual numerical relationships between pairs of objects. More precisely, we propose a new fuzzyrelational algorithm, based on the popular fuzzy C-means (FCM) algorithm, which does not require any particular restriction on the relation matrix. We describe the application of the algorithm to four real and four synthetic data sets, and show that our algorithm performs better than well-known fuzzy relational clustering algorithms on all these sets.
clustering aims to partition a data set into homogenous groups which gather similar objects. Object similarity, or more often object dissimilarity, is usually expressed in terms of some distance function. This approac...
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clustering aims to partition a data set into homogenous groups which gather similar objects. Object similarity, or more often object dissimilarity, is usually expressed in terms of some distance function. This approach, however, is not viable when dissimilarity is conceptual rather than metric. In this paper, we propose to extract the dissimilarity relation directly from the available data. To this aim, we train a feedforward neural network with some pairs of points with known dissimilarity. Then, we use the dissimilarity measure generated by the network to guide a new unsupervised fuzzyrelationalclustering algorithm. An artificial data set and a real data set are used to show how the clustering algorithm based on the neural dissimilarity outperforms some widely used (possibly partially supervised) clusteringalgorithms based on spatial dissimilarity.
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