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CDEC:a constrained deep embedded clustering

International Journal of Intelligent Computing and Cybernetics 2021年第4期14卷 686-701页

作者： Elham Amirizadeh Reza Boostani Department of Computer Sciences and Engineering Shiraz UniversityShirazIran

Purpose-The aim of this study is to propose a deep neural network(DNN)method that uses side information to improve clustering results for big datasets;also,the authors show that applying this information improves the performance of clustering and also increase the speed of the network training convergence.Design/methodology/approach-In data mining,semisupervised learning is an interesting approach because good performance can be achieved with a small subset of labeled data;one reason is that the data labeling is expensive,and semisupervised learning does not need all labels.One type of semisupervised learning is constrained clustering;this type of learning does not use class labels for clustering.Instead,it uses information of some pairs of instances(side information),and these instances maybe are in the same cluster(must-link[ML])or in different clusters(cannot-link[CL]).Constrained clustering was studied extensively;however,little works have focused on constrained clustering for big datasets.In this paper,the authors have presented a constrained clustering for big datasets,and the method uses a DNN.The authors inject the constraints(ML and CL)to this DNN to promote the clustering performance and call it constrained deep embedded clustering(CDEC).In this manner,an autoencoder was implemented to elicit informative low dimensional features in the latent space and then retrain the encoder network using a proposed Kullback-Leibler divergence objective function,which captures the constraints in order to cluster the projected samples.The proposed CDEC has been compared with the adversarial autoencoder,constrained 1-spectral clustering and autoencoder t k-means was applied to the known MNIST,Reuters-10k and USPS datasets,and their performance were assessed in terms of clustering accuracy.Empirical results confirmed the statistical superiority of CDEC in terms of clustering accuracy to the counterparts.Findings-First of all,this is the first DNN-constrained clustering that uses side information to improve the performanc

关键词： Deep neural networks clustering Constrained clustering Big data Denoising autoencoder Kullback-Leibler divergence Constrained deep embedded clustering(CDEC)

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Stereotypy and variability of social calls among clustering female big-footed myotis(Myotis macrodactylus)

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Zoological Research 2018年第2期39卷 114-122页

作者： Yan-Hong Xiao Lei Wang Joseph R.Hoyt Ting-Lei Jiang Ai-Qing Lin Jiang Feng Jilin Provincial Key Laboratory of Animal Resource Conservation and Utilization Northeast Normal University Changchun Jilin 130117 China School of Animal Science & Technology Jilin Agricultural University Changchun Jilin 130118 China Department of Ecology and Evolutionary Biology University of California Santa Cruz CA 95064 USA

Echolocating bats have developed advanced auditory perception systems, predominantly using acoustic signaling to communicate with each other They can emit a diverse range of social calls in complex behavioral contexts. This study examined the vocal repertoire of five pregnant big-footed myotis bats （Myotis macrodactylus）. In the process of clustering the last individual to return to the colony （LI） emitted social calls that correlated with behavior, as recorded on a PC-based digital recorder. These last individuals could emit 10 simple monosyllabic and 27 complex multisyllabic types of calls, constituting four types of syllables. The social calls were composed of highly stereotyped syllables, hierarchically organized by a common set of syllables. However, intra-specific variation was also found in the number of syllables, syllable order and patterns of syllable repetition across call renditions. Data were obtained to characterize the significant individual differences that existed in the maximum frequency and duration of calls. Time taken to return to the roost was negatively associated with the diversity of social calls. Our findings indicate that variability in social calls may be an effective strategy taken by individuals during reintegration into clusters of female M. macrodactylus.

关键词： Social calls clustering Big-footed myotis

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Cardinality compensation method based on information-weighted consensus filter using data clustering for multi-target tracking

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Chinese Journal of Aeronautics 2019年第9期32卷 2164-2173页

作者： Sunyoung KIM Changho KANG Changook PARK Department of Mechanical and Aerospace Engineering Seoul National UniversitySeoul 08826Republic of Korea BK21+Transformative Training Program for Creative Mechanical and Aerospace Engineers Seoul National UniversitySeoul 08826Republic of Korea Automation and Systems Research Institute Seoul 08826Republic of Korea Institute of Advanced Aerospace Technology Seoul 08826Republic of Korea

In this paper, a cardinality compensation method based on Information-weighted Consensus Filter(ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hypothesis Density(CPHD) filter. Although the joint propagation of the intensity and the cardinality distribution in the CPHD filter process allows for more reliable estimation of the cardinality(target number) than the PHD filter, tracking loss may occur when noise and clutter are high in the measurements in a practical situation. For that reason, the cardinality compensation process is included in the CPHD filter, which is based on information fusion step using estimated cardinality obtained from the CPHD filter and measured cardinality obtained through data clustering. Here, the ICF is used for information fusion. To verify the performance of the proposed method, simulations were carried out and it was confirmed that the tracking performance of the multi-target was improved because the cardinality was estimated more accurately as compared to the existing techniques.

关键词： Cardinality compensation Cardinalized probability hypothesis density filter Data clustering Information-weighted consensus filter Multi-target tracking

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Robust clustering with Topological Graph Partition

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Chinese Journal of Electronics 2019年第1期28卷 76-84页

作者： WANG Shuliang LI Qi YUAN Hanning GENG Jing DAI Tianru DENG Chenwei School of software Beijing Institute of Technology School of Information and Electronics Beijing Institute of Technology

clustering is fundamental in many fields with big data. In this paper, a novel method based on Topological graph partition(TGP) is proposed to group objects. A topological graph is created for a data set with many objects, in which an object is connected to k nearest neighbors. By computing the weight of each object, a decision graph under probability comes into being. A cut threshold is conveniently selected where the probability of weight anomalously becomes large. With the threshold,the topological graph is cut apart into several sub-graphs after the noise edges are cut off, in which a connected subgraph is treated as a cluster. The compared experiments demonstrate that the proposed method is more robust to cluster the data sets with high dimensions, complex distribution, and hidden noises. It is not sensitive to input parameter, we need not more priori knowledge.

关键词： clustering Topological graph partition(TGP) Decision graph under probability Noise edge

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Impact of Distance Measures on the Performance of AIS Data clustering

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Computer Systems Science & Engineering 2021年第1期36卷 69-82页

作者： Marta Mieczyńska Ireneusz Czarnowski Department of Maritime Telecommunications Gdynia Maritime UniversityMorska 81-8781-225GdyniaPoland Department of Information Systems Gdynia Maritime UniversityMorska 81-8781-225GdyniaPoland

Automatic Identification System(AIS)data stream analysis is based on the AIS data of different vessel’s behaviours,including the vessels’routes.When the AIS data consists of outliers,noises,or are incomplete,then the analysis of the vessel’s behaviours is not possible or is limited.When the data consists of outliers,it is not possible to automatically assign the AIS data to a particular vessel.In this paper,a clustering method is proposed to support the AIS data analysis,to qualify noises and outliers with respect to their suitability,and finally to aid the reconstruction of the vessel’s trajectory.In this paper,clustering results have been obtained using selected algorithms,including k-means,k-medoids,and fuzzy c-means.Based on the clustering results,it is possible to decide on the qualification of data with outliers and on their usefulness in the reconstruction of the vessel trajectory.The main aim of this paper is to answer how different distance measures during a clustering process can influence AIS data clustering quality.The main core question is whether or not they have an impact on the process of reconstruction of the vessel trajectories when the data are damaged.The research question during the computational experiments asked whether or not distance measure influence AIS data clustering quality.The computational experiments have been carried out using original AIS data.In general,the experiment and the results confirm the usefulness of the cluster-based analysis when the data include outliers that are derived from the natural environment.It is also possible to monitor and to analyse AIS data using clustering when the data include outliers.The computational experiment results confirm that the k-means with Euclidean distance has the best performance.

关键词： AIS SAT-AIS AIS data stream clustering maritime data analysis

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Modeling Bacterial Species: Using Sequence Similarity with clustering Techniques

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Computers, Materials & Continua 2021年第8期68卷 1661-1672页

作者： Miguel-Angel Sicilia Elena García-Barriocanal Marçal Mora-Cantallops Salvador Sánchez-Alonso Lino González University of Alcalá Alcaláde Henares(Madrid)28871Spain Camilo JoséCela University Madrid28007Spain

Existing studies have challenged the current definition of named bacterial species,especially in the case of highly recombinogenic bacteria.This has led to considering the use of computational procedures to examine potential bacterial clusters that are not identified by species naming.This paper describes the use of sequence data obtained from MLST databases as input for a k-means algorithm extended to deal with housekeeping gene sequences as a metric of similarity for the clustering process.An implementation of the k-means algorithm has been developed based on an existing source code implementation,and it has been evaluated against MLST data.Results point out to potential bacterial clusters that are close to more than one different named species and thus may become candidates for alternative classifications accounting for genotypic information.The use of hierarchical clustering with sequence comparison as similarity metric has the potential to find clusters different from named species by using a more informed cluster formation strategy than a conventional nominal variant of the algorithm.

关键词： clustering bacterial species k-means sequence alignment

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A Novel Hybrid clustering Based Transmission Protocol for Wireless Body Area Networks

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Computers, Materials & Continua 2021年第11期69卷 2459-2473页

作者： Neelam Sharma Harshita Chadha Karan Singh B.M.Singh Nitish Pathak Research Scholar Computer Science and EngineeringUttarakhand Technical University(UTU)DehradunIndia Computer Science and Engineering Maharaja Agrasen Institute of TechnologyNew DelhiIndia School of Computer&Systems Sciences Jawaharlal Nehru University(JNU)DelhiIndia College of Engineering Roorkee UTUDehradunIndia Department of Information Technology KIET Group of InstitutionsGhaziabadIndia

Wireless sensor networks are a collection of intelligent sensor devices that are connected to one another and have the capability to exchange information packets amongst themselves.In recent years,this field of research has become increasingly popular due to the host of useful applications it can potentially serve.A deep analysis of the concepts associated with this domain reveals that the two main problems that are to be tackled here are throughput enhancement and network security improvement.The present article takes on one of these two issues namely the throughput enhancement.For the purpose of improving network productivity,a hybrid clustering based packet propagation protocol has been proposed.The protocol makes use of not only clustering mechanisms of machine learning but also utilizes the traditional forwarding function approach to arrive at an optimum model.The result of the simulation is a novel transmission protocol which significantly enhances network productivity and increases throughput value.

关键词： Forwarding function wireless sensor networks WSNs WBANs energy characteristics clustering hybrid mechanism dissipated energy network stability

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A maximum margin clustering algorithm based on indefinite kernels

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Frontiers of Computer Science 2019年第4期13卷 813-827页

作者： Hui XUE Sen LI Xiaohong CHEN Yunyun WANG School of Computer Science and Engineering Southeast University Nanjing 210096 Chin Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing 210096 China College of Science Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Department of Computer Science and Engineering Nanjing University of Posts and Telecommunications Nanjing 210046 China

Indefinite kernels have attracted more and more attentions in machine learning due to its wider application scope than usual positive definite kernels. However, the research about indefinite kernel clustering is relatively scarce. Furthermore, existing clustering methods are mainly designed based on positive definite kernels which are incapable in indefinite kernel scenarios. In this paper, we propose a novel indefinite kernel clustering algorithm termed as indefinite kernel maximum margin clustering (IKMMC) based on the state-of-the-art maximum margin clustering (MMC) model. IKMMC tries to find a proxy positive definite kernel to approximate the original indefinite one and thus embeds a new F-norm regularizer in the objective function to measure the diversity of the two kernels, which can be further optimized by an iterative approach. Concretely, at each iteration, given a set of initial class labels, IKMMC firstly transforms the clustering problem into a classification one solved by indefinite kernel support vector machine (IKSVM) with an extra class balance constraint and then the obtained prediction labels will be used as the new input class labels at next iteration until the error rate of prediction is smaller than a prespecified tolerance. Finally, IKMMC utilizes the prediction labels at the last iteration as the expected indices of clusters.Moreover, we further extend IKMMC from binary clustering problems to more complex multi-class scenarios. Experimental results have shown the superiority of our algorithms.

关键词： indefinite kernel maximum margin clustering support vector machine kernel method

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Construction mechanism of whitenization weight function and its application in grey clustering evaluation

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Journal of Systems Engineering and Electronics 2019年第1期30卷 121-131页

作者： XIE Naiming SU Bentao CHEN Nanlei College of Economics and Management Nanjing University of Aeronautics and Astronautics

The clustering evaluation can be used to scientifically classify the objects to be evaluated according to the information aggregation of various evaluation rules. In grey weighted clustering evaluation, the index clustering rule relies on the construction of the whitenization weight function, while the existing construction method of the linear function lacks the construction mechanism analysis and validity explanation. A normative construction principle is put forward by analyzing the construction mechanism of the function. Through proving the normative principle of the function,the basic modal function(BMF) is proposed and characterized by different function forms. Then, a new type of the whitenization weight function and its grey clustering evaluation model algorithm are given by studying the mechanism and nature of the construction of different forms of the function. Finally, the comparative study for self-innovation capability of defense science and technology industry(DSTI) is taken as an example. The results show that the different construction ways of the function have an effect on the clustering result. The proposed construction mechanism can better explain the index clustering rules and evaluation effectiveness,which will perfect the theoretical system of grey clustering evaluation and be applied to practice effectively.

关键词： whitenization weight function grey system theory grey clustering evaluation.

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Enhancing subspace clustering based on dynamic prediction

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Frontiers of Computer Science 2019年第4期13卷 802-812页

作者： Ratha PECH Dong HAO Hong CHENG Tao ZHOU Complex Lab University of Electronic Science and Technology of China Chengdu 611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 China Center for Robotics University of Electronic Science and Technology of China Chengdu 611731 China

In high dimensional data, many dimensions are irrelevant to each other and clusters are usually hidden under noise. As an important extension of the traditional clustering, subspace clustering can be utilized to simultaneously cluster the high dimensional data into several subspaces and associate the low-dimensional subspaces with the corresponding points. In subspace clustering, it is a crucial step to construct an affinity matrix with block-diagonal form, in which the blocks correspond to different clusters. The distance-based methods and the representation-based methods are two major types of approaches for building an informative affinity matrix. In general, it is the difference between the density inside and outside the blocks that determines the efficiency and accuracy of the clustering. In this work, we introduce a well-known approach in statistic physics method, namely link prediction, to enhance subspace clustering by reinforcing the affinity matrix. More importantly, we introduce the idea to combine complex network theory with machine learning. By revealing the hidden links inside each block, we maximize the density of each block along the diagonal, while restrain the remaining non-blocks in the affinity matrix as sparse as possible. Our method has been shown to have a remarkably improved clustering accuracy comparing with the existing methods on well-known datasets.

关键词： subspace clustering link prediction blockdiagonal matrix low-rank representation sparse representation.

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