Search

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]

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. [ABSTRACT FROM AUTHOR]

Highlights • A novel method is proposed for the prediction of N6-methyladenosine sites. • We construct the combination of deep features and the original features to make the results better. • M6A-PXGB first uses particle swarm optimization to optimize parameters in XGBoost model. • Overall accuracy of the M6A-PXGB is 77.16% to sensitivity value 76.38%, specificity value 75.98%, AUC value 0.839 and MCC value 0.535. Abstract N6-methyladenosine (m6A) is the one of the most important RNA modifications, playing the role of splicing events, mRNA exporting and stability to cell differentiation. Because of wide distribution of m6A in genes, identification of m6A sites in RNA sequences has significant importance for basic biomedical research and drug development. High-throughput laboratory methods are time consuming and costly. Nowadays, effective computational methods are much desirable because of its convenience and fast speed. Thus, in this article, we proposed a new method to improve the performance of the m6A prediction by using the combined features of deep features and original features with extreme gradient boosting optimized by particle swarm optimization (PXGB). The proposed PXGB algorithm uses three kinds of features, i.e., position-specific nucleotide propensity (PSNP), position-specific dinucleotide propensity (PSDP), and the traditional nucleotide composition (NC). By 10-fold cross validation, the performance of PXGB was measured with an AUC of 0.8390 and an MCC of 0.5234. Additionally, PXGB was compared with the existing methods, and the higher MCC and AUC of PXGB demonstrated that PXGB was effective to predict m6A sites. The predictor proposed in this study might help to predict more m6A sites and guide related experimental validation. [ABSTRACT FROM AUTHOR]