Multi-kernel graph fusion for spectral clustering

Many methods of multi-kernel clustering have a bias to power base kernels by ignoring other kernels. To address this issue, in this paper, we propose a new method of multi-kernel graph fusion based on min–max optimization (namely MKGF-MM) for spectral clustering by making full use of all base kernels. Specifically, the proposed method investigates a novel min–max weight strategy to capture the complementary information among all base kernels. As a result, every base kernel contributes to the construction of the fusion graph from all base kernels so that the quality of the fusion graph is guaranteed. In addition, we design an iterative optimization method to solve the proposed objective function. Furthermore, we theoretically prove that our optimization method achieves convergence. Experimental results on real medical datasets and scientific datasets demonstrate that the proposed method outperforms all comparison methods and the proposed optimization method achieves fast convergence.     

One-step spectral rotation clustering for imbalanced high-dimensional data

The class distribution of imbalanced data sets is skewed in practical application. As traditional clustering methods mainly are designed for improving the overall learning performance, the majority class usually tends to be clustered and the minority class which is more valuable maybe ignored. Moreover, existing clustering methods can be limited for the performance of imbalanced and high-dimensional domains. In this paper, we present one-step spectral rotation clustering for imbalanced high-dimensional data (OSRCIH) by integrating self-paced learning and spectral rotation clustering in a unified learning framework, where sample selection and dimensionality reduction are simultaneously considered with mutual and iterative update. Specifically, the imbalance problem is considered by selecting the same number of training samples from each intrinsic group of the training data, where the sample-weight vector is obtained by self-paced learning. Moreover, dimensionality reduction is conducted by combining subspace learning and feature selection. Experimental analysis on synthetic datasets and real datasets showed that OSRCIH could recognize and enhance the weight of important samples and features so as to avoid the clustering method in favor of the majority class and to improve effectively the clustering performance.     

珠三角地方政府在集群创新中的作用

在回顾产业集群创新研究的基础上,分析了珠三角地方政府在产业集群创新中的作用,归纳出了3种创新模式：政府供给型创新、政府扶持型创新、政府引进型创新。3种模式各有利弊,但均是地方政府根据当地情况和产业特色而选择的创新模式,对当地产业集群创新起到了非常大的推动作用。     

Learning a subspace for clustering via pattern shrinking

Clustering is a basic technique in information processing. Traditional clustering methods, however, are not suitable for high dimensional data. Thus, learning a subspace for clustering has emerged as an important research direction. Nevertheless, the meaningful data are often lying on a low dimensional manifold while existing subspace learning approaches cannot fully capture the nonlinear structures of hidden manifold. In this paper, we propose a novel subspace learning method that not only characterizes the linear and nonlinear structures of data, but also reflects the requirements of following clustering. Compared with other related approaches, the proposed method can derive a subspace that is more suitable for high dimensional data clustering. Promising experimental results on different kinds of data sets demonstrate the effectiveness of the proposed approach.     

Bi-level weighted multi-view clustering via hybrid particle swarm optimization

Many problems in data mining involve datasets with multiple views where the feature space consists of multiple feature groups. Previous studies employed view weighting method to find a shared cluster structure underneath different views. However, most of these studies applied gradient optimization method to optimize the cluster centroids and feature weights iteratively and made the final partition local optimal. In this work, we proposed a novel bi-level weighted multi-view clustering method with emphasizing fuzzy weighting on both view and feature. Furthermore, an efficient global search strategy that combines particle swarm optimization and gradient optimization was proposed to solve the induced non-convex loss function. In the experimental analysis, the performance of the proposed method was compared with five state-of-the-art weighted clustering algorithms on three real-world high-dimensional multi-view datasets.     

A scalable privacy-preserving recommendation scheme via bisecting k-means clustering

Privacy-preserving collaborative filtering is an emerging web-adaptation tool to cope with information overload problem without jeopardizing individuals’ privacy. However, collaborative filtering with privacy schemes commonly suffer from scalability and sparseness as the content in the domain proliferates. Moreover, applying privacy measures causes a distortion in collected data, which in turn defects accuracy of such systems. In this work, we propose a novel privacy-preserving collaborative filtering scheme based on bisecting k-means clustering in which we apply two preprocessing methods. The first preprocessing scheme deals with scalability problem by constructing a binary decision tree through a bisecting k-means clustering approach while the second produces clones of users by inserting pseudo-self-predictions into original user profiles to boost accuracy of scalability-enhanced structure. Sparse nature of collections are handled by transforming ratings into item features-based profiles. After analyzing our scheme with respect to privacy and supplementary costs, we perform experiments on benchmark data sets to evaluate it in terms of accuracy and online performance. Our empirical outcomes verify that combined effects of the proposed preprocessing schemes relieve scalability and augment accuracy significantly.     

Control of distributed segmentation of indoor point cloud via homogenization clustering network

Indoor point cloud segmentation is necessary for many applications including object identification for indoor navigation, facility management and so on. In order to construct accurate segmentation system of indoor point cloud, this paper improves cloth simulation and uses it to control the segmentation of ceiling and floor. Simultaneously, we obtain the wall points according to the points’ density. We mainly introduce indoor objects control segmentation via clustering strategy. First, we generate the cutoff distance according to the angular resolution and scan distance. Second, we use exponential function to determine the local density. Third, the cluster center is determined according to the magnitude of product of local density and distance which are normalized. Finally, the points affiliated to a cluster are controlled one by one according to the distance between point and cluster center. Segmentation of objects is then realized based on the established clusters. Experiments indicate that the proposed algorithm achieves a competitive performance when compared with several state-of-the-art algorithms. The performance of the proposed method and the accuracy of distributed segmentation are affected by the degree of closeness between objects.     

Maximum entropy method spectral estimation applied to power cable diagnostics via cepstrum processing

The Cepstrum processing method has been used on power cables (1, 2) for determining regions of damage. The method consists of: (1) observing the spectrum of an original broad band signal source, (2) introducing the signal into the cable, (3) computing the change in the observed spectrum (as a result of echos from regions of cable non-uniformity) when the signal is injected into the cable, and finally (4) computing the power-spectrum of the change in observed spectrum. A limitation of this technique is the use of band-limiting spectrum analyzers. Their limited bandwidth reduces range resolution estimation when used with the Fast Fourier Transform (FFT) technique. The maximum entropy method (MEM) is a more useful spectral estimator for this measurement technique. Examples are presented which show a comparison of the FFT and MEM techniques applied to practical cables.     

黑土土壤水分光谱响应特征与模型

为揭示土壤水分对土壤反射率的影响机理,并为其他土壤参数遥感监测提供理论支持,以吉林省德惠市黑土野外、室内高光谱反射率为研究对象,运用光谱分析方法与统计分析方法,分析土壤水分光谱特征,建立土壤水分光谱模型,得出以下结论：土壤光谱反射率在400-2500 nm范围内主要有5个吸收谷,随着含水量的增加,土壤光谱吸收谷的面积增大;而秸秆光谱反射率在土壤前2个吸收谷附近没有明显的吸收特征;未翻耕土壤由于秸秆残茬的影响,第二吸收谷左右两部分面积比例增大,这一特征可用于判别土壤是否翻种;土壤表层0-5cm含水量与反射光谱数据的相关性强于5-10cm;土壤含水量与土壤光谱反射率之间是指数或线性关系;土壤含水量高光谱模型稳定性较好、精度较高,可以用于土壤含水量的速测。     

A simple form synthesis of linear time-variant digital filter via spectral decomposition of its impulse response

The paper describes a new technique to synthesize a finite impulse response of linear time-variant (LTV) digital filter. First, a finite impulse response is decomposed into a sum of products of two orthogonal sequences. The direct implementation of the decomposed impulse response leads to the parallel connection of linear time-invariant (LTI) digital filters, followed by time-varying multipliers. A simple filter structure is obtained by properly modifying the sequences to realize the parallel form structure as a cascade connection of first- or second-order recursive LTI filters. The structure is easy to implement on a computer and saves computation time. Numerical examples illustrating the technique are included.     

Multi-view co-attention network for fake news detection by modeling topic-specific user and news source credibility

The wide spread of fake news and its negative impacts on society has attracted a lot of attention to fake news detection. In existing fake news detection methods, particular attention has been paid to the credibility of the users sharing the news on social media, and the news sources based on their level of participation in fake news dissemination. However, these methods have ignored the important role of news topical perspectives (like political viewpoint) in users'/sources' decisions to share/publish the news. These decisions are associated with the viewpoints shared by the echo-chamber that the users belong to, i.e., users' Socio-Cognitive (SC) biases, and the news sources' partisan bias. Therefore, the credibility of users and news sources are varied in different topics according to the mentioned biases; which are completely ignored in current fake news detection studies. In this paper, we propose a Multi-View Co-Attention Network (MVCAN) that jointly models the latent topic-specific credibility of users and news sources for fake news detection. The key idea is to represent news articles, users, and news sources in a way that the topical viewpoints of news articles, SC biases of users which determines the users' viewpoints in sharing news, and the partisan bias of news sources are encoded as vectors. Then a novel variant of the Multi-Head Co-Attention (MHCA) mechanism is proposed to encode the joint interaction from different views, including news-source and news-user to implicitly model the credibility of users and the news sources based on their interaction in real and fake news spreading on the news topic. We conduct extensive experiments on two public datasets. The results show that MVCAN significantly outperforms other state-of-the-art methods and outperforms the best baselines by 3% on average in terms of F1 and Accuracy.     

高校科技统计指标动态聚类与静态聚类比较分析研究

聚类分析用于高校科技活动的评价体现了简捷、实用的特点,是高校科技实力评估的一种可行的新途径。本文以江苏省15所部委和省属重点高校1998年至2002年科技统计指标为例,分别用动态聚类和静态聚类的方法进行分析,研究动态聚类和静态聚类用于高校科技统计指标分析时的不同特点和不同用途。     

Contrastive and attentive graph learning for multi-view clustering

Graph-based multi-view clustering aims to take advantage of multiple view graph information to provide clustering solutions. The consistency constraint of multiple views is the key of multi-view graph clustering. Most existing studies generate fusion graphs and constrain multi-view consistency by clustering loss. We argue that local pair-view consistency can achieve fine-modeling of consensus information in multiple views. Towards this end, we propose a novel Contrastive and Attentive Graph Learning framework for multi-view clustering (CAGL). Specifically, we design a contrastive fine-modeling in multi-view graph learning using maximizing the similarity of pair-view to guarantee the consistency of multiple views. Meanwhile, an Att-weighted refined fusion graph module based on attention networks to capture the capacity difference of different views dynamically and further facilitate the mutual reinforcement of single view and fusion view. Besides, our CAGL can learn a specialized representation for clustering via a self-training clustering module. Finally, we develop a joint optimization objective to balance every module and iteratively optimize the proposed CAGL in the framework of graph encoder–decoder. Experimental results on six benchmarks across different modalities and sizes demonstrate that our CAGL outperforms state-of-the-art baselines.     

Adaptive and structured graph learning for semi-supervised clustering

This paper proposes a new method for semi-supervised clustering of data that only contains pairwise relational information. Specifically, our method simultaneously learns two similarity matrices in feature space and label space, in which similarity matrix in feature space learned by adopting adaptive neighbor strategy while another one obtained through tactful label propagation approach. Moreover, the above two learned matrices explore the local structure (i.e., learned from feature space) and global structure (i.e., learned from label space) of data respectively. Furthermore, most of the existing clustering methods do not fully consider the graph structure, they can not achieve the optimal clustering performance. Therefore, our method forcibly divides the data into c clusters by adding a low rank restriction on the graphical Laplacian matrix. Finally, a restriction of alignment between two similarity matrices is imposed and all items are combined into a unified framework, and an iterative optimization strategy is leveraged to solve the proposed model. Experiments in practical data show that our method has achieved brilliant performance compared with some other state-of-the-art methods.     

众创空间集群与区域产业结构转型升级

新常态下,我国经济需要通过创新驱动来推动产业升级和经济转型。本文以"苏州工业园区金鸡湖创业长廊"这一众创空间集聚区开展探索式理论建构分析,对众创空间服务区域产业转型过程中涉及的"路径选择"、"机理链条"及"生态系统"的内在逻辑进行系统阐释。研究发现:众创空间扶植创业活动只是整个机理链的起点,在良好的创新创业生态环境培育下,创新网络与创业资源、科技产业的互动机制能够给区域经济带来独特的竞争优势,并成为区域产业转型升级的持续推动力。     

Using clustering and classification approaches in interactive retrieval

Satisfying non-trivial information needs involves collecting information from multiple resources, and synthesizing an answer that organizes that information. Traditional recall/precision-oriented information retrieval focuses on just one phase of that process: how to efficiently and effectively identify documents likely to be relevant to a specific, focused query. The TREC Interactive Track has as its goal the location of documents that pertain to different instances of a query topic, with no reward for duplicated coverage of topic instances. This task is similar to the task of organizing answer components into a complete answer. Clustering and classification are two mechanisms for organizing documents into groups. In this paper, we present an ongoing series of experiments that test the feasibility and effectiveness of using clustering and classification as an aid to instance retrieval and, ultimately, answer construction. Our results show that users prefer such structured presentations of candidate result set to a list-based approach. Assessment of the structured organizations based on the subjective judgement of the experiment subjects suggests that the structured organization can be more effective; however, assessment based on objective judgements shows mixed results. These results indicate that a full determination of the success of the approach depends on assessing the quality of the final answers generated by users, rather than on performance during the intermediate stages of answer construction.     

Hybrid clustering for validation and improvement of subject-classification schemes

A hybrid text/citation-based method is used to cluster journals covered by the Web of Science database in the period 2002–2006. The objective is to use this clustering to validate and, if possible, to improve existing journal-based subject-classification schemes. Cross-citation links are determined on an item-by-paper procedure for individual papers assigned to the corresponding journal. Text mining for the textual component is based on the same principle; textual characteristics of individual papers are attributed to the journals in which they have been published. In a first step, the 22-field subject-classification scheme of the Essential Science Indicators (ESI) is evaluated and visualised. In a second step, the hybrid clustering method is applied to classify the about 8300 journals meeting the selection criteria concerning continuity, size and impact. The hybrid method proves superior to its two components when applied separately. The choice of 22 clusters also allows a direct field-to-cluster comparison, and we substantiate that the science areas resulting from cluster analysis form a more coherent structure than the “intellectual” reference scheme, the ESI subject scheme. Moreover, the textual component of the hybrid method allows labelling the clusters using cognitive characteristics, while the citation component allows visualising the cross-citation graph and determining representative journals suggested by the PageRank algorithm. Finally, the analysis of journal ‘migration’ allows the improvement of existing classification schemes on the basis of the concordance between fields and clusters.     

地方性克汀病家庭聚集性调查和遗传度估计

目的:通过地方性克汀病家庭聚集性调查,对其遗传度进行估计;方法:选取内蒙古自治区和林格尔县、乌兰察布盟凉城县、兴安盟突泉县和赤峰市松山区为调查对象,进行克汀病系谱调查和遗传度估计;结果:地方性克汀病一级亲属、二级亲属和三级亲属患病率分别为16.2%,3.5%和15%,加权平均遗传度为65.4%±4.2%;结论:地方性克汀病存在明显的家庭聚集性,其遗传方式基本符合多基因遗传的特征。