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International Conference On Futuristic Trends in Computational Analysis and knowledge Management

作者： Ahalya, G. Pandey, Hari Mohan Amity Univ ASET CSE Dept Noida India

ISBN: (纸本)9781479984336

In the current world, there is a need to analyze and extract information from data. clustering is one such analytical method which involves the distribution of data into groups of identical objects. Every group is known as a cluster, which consists of objects that have affinity within the cluster and disparity with the objects in other groups. This paper is intended to examine and evaluate various data clustering algorithms. The two major categories of clustering approaches are partition and hierarchical clustering. The algorithms which are dealt here are: k-means clustering algorithm, hierarchical clustering algorithm, density based clustering algorithm, self-organizing map algorithm, and expectation maximization clustering algorithm. All the mentioned algorithms are explained and analyzed based on the factors like the size of the dataset, type of the data set, number of clusters created, quality, accuracy and performance. This paper also provides the information about the tools which are used to implement the clustering approaches. The purpose of discussing the various software/tools is to make the beginners and new researchers to understand the working, which will help them to come up with new product and approaches for the improvement.

关键词： clustering Expectation maximization clustering algorithm Hierarchical clustering k-means clustering algorithm Self-organization maps algorithm density based clustering algorithm

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Blind continuous hidden Markov model-based spectrum sensing and recognition for primary user with multiple power levels

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IET COMMUNICATIONS 2015年第11期9卷 1396-1403页

作者： Liu, Boyang Li, Zan Si, Jiangbo Zhou, Fuhui Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China

Spectrum sensing has been well studied because of its significance in cognitive radio. Different from the existing works which a primary user (PU) is assumed to have only one constant transmit power, a more practical scenario that the PU transmitting with multiple power levels is considered. A continuous hidden Markov model (CHMM)-based blind algorithm for not only detecting the presence of PU but also recognising the transmit power level of the PU is proposed. The training problem of CHMM is solved by combining the wavelet singularity detection with k-means clustering algorithm. An effective method for estimation of the number of power levels is proposed. Two different strategies are designed to perform spectrum sensing. Simulation results show the efficiency of the proposed algorithm.

关键词： hidden Markov models radio spectrum management cognitive radio wavelet transforms signal detection pattern clustering blind continuous hidden Markov model-based spectrum sensing primary user multiple power level spectrum recognition cognitive radio CHMM-based blind algorithm PU transmit power level recognition wavelet singularity detection k-means clustering algorithm

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A Method for Coherency Identification Based on Singular Value Decomposition

A Method for Coherency Identification Based on Singular Valu...

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IEEE Power and Energy Society General Meeting

作者： Qiaomu Zhu Jinfu Chen Xianzhong Duan Xin Sun Yinhong Li Dongyuan Shi State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology

ISBN: (纸本)9781509041695

A novel method to identify coherent generator groups based on singular value decomposition (SVD) in power system is presented in this paper. Via the SVD algorithm, the high-dimensional real-time rotor-angle data of generators is projected into low-dimensional subspace, and the key characteristic coefficients indicating the information of coherency is obtained. Then, the coherency identification is realized by running k-means clustering algorithm on the coefficients. The dimensionality reduction for clustering algorithm by SVD speeds up the coherency identification. Using the real-time rotor-angle data, the proposed method has the potential to be applied to measured data to implement coherency identification. The effectiveness of this method is demonstrated by the simulation results on IEEE 39-bus system.

关键词： Coherency identification k-means clustering algorithm Real-time rotor angles Singular value decomposition (SVD) Singular value decomposition Swine vesicular disease clustering algorithms k-means algorithm dimensionality reduction angular data Authentication dynamos power systems

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An Adaptive Remaining Life Prediction for Rolling Element Bearings

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JOURNAL OF FAILURE ANALYSIS AND PREVENTION 2015年第1期15卷 82-89页

作者： Zhang, Shuai Zhang, Yongxiang Zhu, Jieping Naval Univ Engn Power Engn Marine Engn Wuhan 430033 Hubei Peoples R China

In order to select the effective health index and build reasonably the prediction model for prognostics, a new approach is proposed. The generative topographic mapping-based negative likelihood probability is used as the health index, and k-means clustering algorithm is employed for state division. The adaptive prediction model based on Markov model and least mean square algorithm is built by the historical data and the online monitoring data. According to the given threshold, the remaining life can be captured. Based on experimental verification, the results indicate that the selected health index is able to effectively reflect the condition of rolling bearings and the proposed model shows high prediction accuracy in comparison to the common one.

关键词： Rolling bearings Remaining life prediction Generative topographic mapping k-means clustering algorithm An adaptive prediction model

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Proposing An Evolutionary Method Based On Maximization Precision Of Group Recommender Systems 7

Proposing An Evolutionary Method Based On Maximization Preci...

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2015 7th Conference on Information and knowledge Technology (IkT)

作者： Loveymi, Shakib Hamzeh, Ali Shiraz Univ Dept Comp Sci & Engn & IT Shiraz Iran

ISBN: (纸本)9781467374859

in this paper we proposed an evolutionary algorithm to maximize precision of group recommender systems and reducing the online calculations. In this method we try to build a transition matrix that's made by an evolutionary algorithm and then we multiply this transition matrix with user-item rate matrix. By this action we go to a reduced dimension space. The characteristic of this space is that the users that are really more similar, would be closer to each other. Also because the dimension of the user-item matrix has been reduced, the online calculations are hugely reduced and if we have a new user, we can easily multiply his rates on the transition matrix and find out that he has to be in which group. We used the average real rate of group users to an item as a metric to evaluate how much this item is suitable for this specific group. At end we compare this method with other methods that also reduce the dimension on the various datasets. Then we show that our method works better. Finally we have a discussion about weakness and strength of our method.

关键词： Group Recommender Systems Evolutionary algorithm Transition matrix k-means clustering algorithm Average Precision

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A Credit Rating Model for Enterprises Based on kernel Density Estimation and k-means clustering algorithm

A Credit Rating Model for Enterprises Based on Kernel Densit...

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工程和商业管理国际学术会议

作者： Zhang Mu 1,2 , Zhou Zong-fang 1 1. School of Management and Economics, University of Electronic Science and Technology of China, UESTC, Chengdu, China 2. School of Finance, Guizhou College of Finance and Economics, GZCFE, Guiyang, China

This paper proposes a new credit rating model for enterprises based on kernel density estimation and k-means clustering algorithm. Firstly, using the principal component analysis, the comprehensive credit score of each sample is obtained. Secondly, the kernel density estimation method is applied to analyze the comprehensive credit score series, and then the initial cluster centers are determined according to the local maximum points of density function. Finally, using k-means clustering algorithm, the final cluster centers are obtained, and the credit grades are divided. Thus, the credit rating for enterprises is realized. Through a specific example, it is proved that the model proposed by this paper is feasible and effective.

关键词： credit rating principal component analysis kernel density estimation initial cluster centers k-means clustering algorithm

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Fuzzy Granular Classifier Approach for Spam Detection 7th

Fuzzy Granular Classifier Approach for Spam Detection

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7th International Conference on Computational Collective Intelligence (ICCCI)

作者： Salehi, Saber Selamat, Ali krejcar, Ondrej kuca, kamil Univ Teknol Malaysia UTM IRDA Digital Media Ctr Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Comp Skudai 81310 Johor Malaysia Univ Hradec Kralove Ctr Basic & Appl Res Fac Informat & Management Hradec Kralove 50003 Czech Republic

ISBN: (纸本)9783319243061;9783319243054

Spam email problem is a major shortcoming of email technology for computer security. In this research, a granular classifier model is proposed to discover hyperboxes in the geometry of information granules for spam detection in three steps. In the first step, the k-means clustering algorithm is applied to find the seed_points to build the granular structure of the spam and non-spam patterns. Moreover, applying the interval analysis through the high homogeneity of the patterns captures the key part of the spam and non-spam classifiers' structure. In the second step, PSO algorithm is hybridized with the k-means to optimize the formalized information granules' performance. The proposed model is evaluated based on the accuracy, misclassification and coverage criteria. Experimental results reveal that the performance of our proposed model is increased through applying Particle Swarm Optimization and fuzzy set.

关键词： Spam detection Hyperbox geometry of classifiers Granular classifier Membership functions Particle Swarm Optimization k-means clustering algorithm

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Damaged road extracting with high resolution aerial image of post-earthquake

Damaged road extracting with high resolution aerial image of...

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International Conference on Intelligent Earth Observing and Applications (IEOAs)

作者： Zheng, Zezhong Pu, Chengjun Zhu, Mingcang Xia, Jun Zhang, Xiang Liu, Yalan Li, Jiang Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Sichuan Peoples R China Guangxi Key Lab Spatial Informat & Geomat Guilin 541004 Guangxi Peoples R China Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Hubei Peoples R China Chengdu Univ Technol Key Lab Geosci Spatial Informat Technol Minist Land & Resources China Chengdu 610059 Sichuan Peoples R China Beijing Normal Univ & Inst Remote Sensing & Digit State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Land & Resources Dept Sichuan Prov Chengdu 610072 Sichuan Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 100094 Peoples R China Old Dominion Univ Dept Elect & Comp Engn Norfolk VA 23529 USA

ISBN: (纸本)9781510600492

With the rapid development of earth observation technology, remote sensing images have played more important roles, because the high resolution images can provide the original data for object recognition, disaster investigation, and so on. When a disastrous earthquake breaks out, a large number of roads could be damaged instantly. There are a lot of approaches about road extraction, such as region growing, gray threshold, and k-means clustering algorithm. We could not obtain the undamaged roads with these approaches, if the trees or their shadows along the roads are difficult to be distinguished from the damaged road. In the paper, a method is presented to extract the damaged road with high resolution aerial image of post-earthquake. Our job is to extract the damaged road and the undamaged with the aerial image. We utilized the mathematical morphology approach and the k-means clustering algorithm to extract the road. Our method was composed of four ingredients. Firstly, the mathematical morphology filter operators were employed to remove the interferences from the trees or their shadows. Secondly, the k-means algorithm was employed to derive the damaged segments. Thirdly, the mathematical morphology approach was used to extract the undamaged road;Finally, we could derive the damaged segments by overlaying the road networks of pre-earthquake. Our results showed that the earthquake, broken in Yaan, was disastrous for the road, Therefore, we could take more measures to keep it clear.

关键词： Damaged road extracting mathematical morphology k-means clustering algorithm high resolution aerial images of post-earthquake

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Phase identification for product quality prediction in batch processes: application to industrial resin production

Phase identification for product quality prediction in batch...

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European Control Conference (ECC)

作者： Depalo, Andrea Barolo, Massimiliano Bezzo, Fabrizio Muradore, Riccardo Univ Verona Dept Comp Sci Altair Lab I-37100 Verona Italy Univ Padua Dept Ind Engn Comp Aided Proc Engn Lab CAPE Lab I-35100 Padua Italy

ISBN: (纸本)9783952426937

This paper addresses the phase identification problem in the development of a soft-sensor for quality variables prediction in batch processes. Batch processes are characterized by time-varying dynamic behavior. This means that input-output variable correlation is not constant during the whole process and so using a single statistical/deterministic model is not usually accurate enough to describe the plant behavior. The multi-phase approach overcomes this problem by grouping process data in different clusters according to their statistical structures. Each phase is then modeled independently using, e.g., the Partial Least Square (PLS) method. In this paper we propose a method to identify process phases based on the k-means clustering algorithm. The main advantages are that this method is generic, automatic and requires no a-priori knowledge of the process. The proposed methodology has been validated on data coming from an industrial plant for production of a resin.

关键词： batch processing (industrial) least mean squares methods product quality resins statistical analysis PLS method batch processes industrial resin production k-means clustering algorithm partial least square method phase identification problem product quality prediction soft-sensor time-varying dynamic behaviror Batch production systems Correlation Product design Quality assessment Resins Temperature measurement Yttrium

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Detecting Anomaly Using the Scene Modeling Based on Time Delayed Statistical Data

Detecting Anomaly Using the Scene Modeling Based on Time Del...

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IEEE International Conference on Information and Automation 2015

作者： Chen, Lixin Guo, Huiwen Wang, Min Chen, Yen-Lun Wu, Xinyu Feng, Wei Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Human Machine Intelligence Synerg Syst Shenzhen Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Robot & Intelligent Syst Shenzhen Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Shenzhen Peoples R China Chinese Univ Hong Kong Shatin Shatin Hong Kong Peoples R China

ISBN: (纸本)9781467391047

We propose a novel approach for the crowd anomaly detection in multiple cameras with non-overlapping view. In this paper, we refer to the activities of crowd in far-field scenes. Firstly, we present a model for learning all of the motion patterns under single camera view, which are regarded as the normal situation. In the surveillance region, we mark the entrances and exits under the single camera view and acquire the crowd flow model by the k-means clustering algorithm. Secondly, we analyze the crowd flow model based on the time delayed statistical data between two camera views. And then we acquire the relative location among the entrances and exits in the different regions. Thirdly, we analyze the crowd transferring probabilistic model on the global scene based on the log-likelihood function and Dirichlet distribution to detect the crowd anomaly. We set up the empirical threshold value of probability P-e. If the probability of detected model is less than P-e, the detected model is marked as the crowd anomaly. Our approach is evaluated on the simulated data set and the real data set in far-field scenes. Experimental results show the anomaly detection is precise.

关键词： Crowd anomaly detection non-overlapping view k-means clustering algorithm log-likelihood function Dirichlet distribution

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