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The possibility based clustering algorithm PCM was first proposed by Krishnapuram and Keller to overcome the noise sensitivity of algorithm FCM (Fuzzy C-Means). However, PCM still suffers from the following weaknesses:(1) the clustering results are strongly dependent on parameter selection and/or initialization; (2) the clustering accuracy is often deteriorated due to its coincident clustering problem;(3) outliers can not be well labeled, which will weaken its clustering performances in real applications. In this study, in order to effectively avoid the above weaknesses, a novel enhanced PCM version (EPCM) is presented. Here, at first a novel strategy of flexible hyperspheric dichotomy is proposed which may partition a dataset into two parts: the main cluster and auxiliary cluster, and is then utilized to construct the objective function of EPCM with some novel constraints. Finally, EPCM is realized by using an alternative optimization approach. The main advantage of EPCM lies in the fact that it can not only avoid the coincident cluster problem by using the novel constraint in its objective function, but also has less noise sensitivity and higher clustering accuracy due to the introduction of the strategy of flexible hyperspheric dichotomy. Our experimental results about simulated and real datasets confirm the above conclusions.

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Based on the analysis of particle swarm optimization algorithm, the particle is described in the quantum space and the potential energy field model is created. And then according to the swarms gregariousness, the quantum-behaved particle swarm optimization (QPSO) algorithm is derived. Within the framework of random algorithms global convergence theorem, the convergence of QPSO algorithm is discussed and is proved to be a kind of global convergence algorithm. Three kinds of control strategy are proposed for the unique parameter of QPSO algorithm and they are tested on five benchmark functions. According to the test results, some conclusions concerning the selection of the parameter are drawn.

For large amount of patent texts, how to extract their keywords in an unsupervised way is a very important problem. In existing methods, only the own information of patent texts is analyzed. In this study, an improved TextRank model is proposed, in which prior public knowledge is effectively utilized. Specifically, two following points are first considered: (1) a TextRank network is constructed for each patent text, (2) a prior knowledge network is constructed based on public dictionary data, in which network edges represent the prior interpretation relationship among all dictionary words in dictionary entries. Then, an improved node rank value evaluation formula is designed for TextRank networks of patent texts, in which prior interpretation information in prior knowledge network are introduced. Finally, patent keywords can be extracted by finding top-k node words with higher node rank values. In our experiments, patent text clustering task is used to examine the performance of proposed method, wherein several comparison experiments are executed. Corresponding results demonstrate that, new method can markedly obtain better performance than existing methods for patent keywords extraction task in an unsupervised way.

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An automatic method for characterizing the parameters of ring slub yarn is presented. Firstly, size parameters of slub yarn were measured by using a capacitance-type sensor, and data was expressed as voltage signals through a data acquisition card (DAQ) installed in a PC. Then the voltage signals of the slub yarn were transferred to a two-dimensional (2D) image. Consequently, the repetition pattern of the slub yarn was determined by analyzing the 2D image using cluster analyses with an amended similarity-based clustering method. The samples were produced using a practical slub yarn production system. Results were validated by comparison of measurement parameters with set parameters.

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