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Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee colony approach. In our approach, we first introduce a one-step k-modes procedure, and then integrate this procedure with the artificial bee colony approach to deal with categorical data. In the search process performed by scout bees, we adopt the multi-source search inspired by the idea of batch processing to accelerate the convergence of ABC-K-Modes. The performance of ABC-K-Modes is evaluated by a series of experiments in comparison with that of the other popular algorithms for categorical data.

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.

Clustering analysis, as an important technique in data mining, aims to identify the nature groups or clusters of data objects in the attribute space. Data objects in real-world applications are commonly described by both numeric and categorical attributes. In this research, considering that the partitional clustering algorithms designed for this type of mixed data are prone to get trapped into local optima and the cuckoo search approach is efficient in solving global optimization problems, we propose CCS-K-Prototypes, a novel partitional Clustering algorithm based on Cuckoo Search and K-Prototypes, for clustering mixed numeric and categorical data. To deal with different types of attributes, we develop a novel representation for candidate solutions, and suggest two formulas for the cuckoo to search for the potential solution around the existing solutions or in the entire attribute space. Finally, the performance of the proposed algorithm is assessed by a series of experiments on five benchmark datasets. INDEX TERMS Data clustering, cuckoo search, mixed data, numeric and categorical attributes.

Although words are basic semantic units in text, phrases, and expressions contain additional information, which is important for text classification. To capture this information, traditional algorithms extract composite features via word sequences or co-occurrences, such as bigrams and termsets, but ignore the influence of stop words and punctuation, which results in huge amounts of weak features. In this paper, we propose a text structure-based algorithm to extract composite features. Termsets that cross punctuation marks or stop words in the text are excluded. To eliminate redundancy, a novel discriminative measure containing two factors is suggested. One is employed to measure the relevancy, while the other is incorporated to increase the values of composite features, whose class frequencies are much smaller than those of their sub-features. The experiments on three benchmark datasets with both a support vector machine and a naive Bayes classifier illustrate the effectiveness of the approach.

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