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Data objects with both numeric and categorical attributes are prevalent in many real-world applications. However, most of the partitional clustering algorithms dealing with such data may trap into local optima. To further promote the performance, a novel clustering algorithm, called ABC-K-Prototypes (Artificial Bee Colony clustering based on K-Prototypes), is presented on the basis of the K-Prototypes algorithm, the search strategy of the artificial bee colony, and the chaos theory. In the presented approach, the one-step k-prototypes procedure is first given, and then this procedure is combined with the search strategy of the artificial bee colony to address the mixed numeric and categorical data. In the search process of scout bees, the chaotic map is utilized to generate chaotic sequences for substituting the random numbers. To accelerate the convergence of the ABC-K-Prototypes algorithm, the multi-source search is adopted in the search process of scout bees. Finally, the performance of the ABC-K-Prototypes algorithm is demonstrated by a series of experiments on mixed numeric and categorical data in comparison with that of the other popular algorithms. [ABSTRACT FROM AUTHOR]

Most of the initialization approaches are dedicated to the partitional clustering algorithms which process categorical or numerical data only. However, in real-world applications, data objects with both numeric and categorical features are ubiquitous. The coexistence of both categorical and numerical attributes make the initialization methods designed for single-type data inapplicable to mixed-type data. Furthermore, to the best of our knowledge, in the existing partitional clustering algorithms designed for mixed-type data, the initial cluster centers are determined randomly. In this paper, we propose a novel initialization method for mixed data clustering. In the proposed method, both the distance and density are exploited together to determine initial cluster centers. The performance of the proposed method is demonstrated by a series of experiments on three real-world datasets in comparison with that of traditional initialization methods. [ABSTRACT FROM AUTHOR]

Abstract: Data objects with mixed numeric and categorical attributes are commonly encountered in real world. The k-prototypes algorithm is one of the principal algorithms for clustering this type of data objects. In this paper, we propose an improved k-prototypes algorithm to cluster mixed data. In our method, we first introduce the concept of the distribution centroid for representing the prototype of categorical attributes in a cluster. Then we combine both mean with distribution centroid to represent the prototype of the cluster with mixed attributes, and thus propose a new measure to calculate the dissimilarity between data objects and prototypes of clusters. This measure takes into account the significance of different attributes towards the clustering process. Finally, we present our algorithm for clustering mixed data, and the performance of our method is demonstrated by a series of experiments on four real-world datasets in comparison with that of traditional clustering algorithms. [Copyright &y& Elsevier]

Abstract: In many applications, data objects are described by both numeric and categorical features. The k-prototype algorithm is one of the most important algorithms for clustering this type of data. However, this method performs hard partition, which may lead to misclassification for the data objects in the boundaries of regions, and the dissimilarity measure only uses the user-given parameter for adjusting the significance of attribute. In this paper, first, we combine mean and fuzzy centroid to represent the prototype of a cluster, and employ a new measure based on co-occurrence of values to evaluate the dissimilarity between data objects and prototypes of clusters. This measure also takes into account the significance of different attributes towards the clustering process. Then we present our algorithm for clustering mixed data. Finally, the performance of the proposed method is demonstrated by a series of experiments on four real world datasets in comparison with that of traditional clustering algorithms. [Copyright &y& Elsevier]