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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]

Background: Acetylation on lysine is a widespread post-translational modification which is reversible and plays a crucial role in some biological activities. To better understand the mechanism, it is necessary to identify acetylation sites in proteins accurately. Computational methods are popular because they are more convenient and faster than experimental methods. In this study, we proposed a new computational method to predict acetylation sites in human by combining sequence features and structural features including physicochemical property (PCP), position specific score matrix (PSSM), auto covariation (AC), residue composition (RC), secondary structure (SS) and accessible surface area (ASA), which can well characterize the information of acetylated lysine sites. Besides, a two-step feature selection was applied, which combined mRMR and IFS. It finally trained a cascade classifier based on SVM, which successfully solved the imbalance between positive samples and negative samples and covered all negative sample information. Results: The performance of this method is measured with a specificity of 72.19% and a sensibility of 76.71% on independent dataset which shows that a cascade SVM classifier outperforms single SVM classifier. Conclusions: In addition to the analysis of experimental results, we also made a systematic and comprehensive analysis of the acetylation data. [ABSTRACT FROM AUTHOR]

The earth-to-air heat exchanger (EAHE) is a remarkable technology that taps into the Earth's subsurface heat to either heat or cool the air circulating within buried underground pipes, which can effectively reduce building energy consumption. In cold-arid regions of northwest China, outdoor climate parameters vary considerably throughout the year, and shallow soil temperatures also fluctuate dramatically. This dynamic thermal environment significantly impacts the practical application of the EAHE system. Furthermore, the operation efficiency of the EAHE system can be influenced by the configuration of system parameters. Therefore, this paper takes an EAHE project in Lanzhou as the research object to investigate the operation performance, energy consumption and economy of EAHE in the cold-arid regions of Northwest China. The research results show that heat exchange potential is high in summer and winter, and the outlet temperature varies more gently in winter. During the summer, the cooling capacity of the system is sufficient to the cooling demands of the building, without using auxiliary equipment for cooling; compared to an air-cooled unit with a COP of 3.2, the system reduces cooling energy consumption by 2063.4 kWh. Moreover, we determined the influence degree and optimal scheme of each factor under different evaluation indexes through orthogonal simulation. The specific parameters of the optimal scheme are as follows: pipe length 20 m, pipe diameter 100 mm, buried depth 4 m, and airflow velocity 7 m/s. Compared with the original scheme, the heat transfer efficiency and heat transfer power of this scheme are increased by 37.91 % and 17.6 %, respectively, and the cost per unit heat transfer power is 11,700 CNY/kW, which is 8000 CNY/kW lower than the original scheme. [ABSTRACT FROM AUTHOR]