Conferences related to Clustering algorithms

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2014 IEEE International Conference On Cluster Computing (CLUSTER)

IEEE Cluster is the main conference on all aspects of cluster computing for computational science

  • 2013 IEEE International Conference on Cluster Computing (CLUSTER)

    The conference will put special focus on campus clusters as the core of the cyberinfrastructure strategy and also campus bridging. Major topics: Systems design/configuration, tools, software, middleware, alogrithms, applications, storage.

  • 2012 IEEE International Conference on Cluster Computing (CLUSTER)

    Systems Design and Configuration Tools, Systems Software, and Middleware Algorithms, Applications and Performance Storage and File Systems

  • 2011 IEEE International Conference on Cluster Computing (CLUSTER)

    Major topics of interest include, but are not limited to: Systems Design and Configuration; Tools, Systems Software, and Middleware; Algorithms, Applications and Performance; Storage and File Systems

  • 2010 IEEE International Conference on Cluster Computing (CLUSTER)

    Cluster2010 welcomes original unpublished paper and poster submissions from researchers in academia, industry, and government, describing innovative research in the field of cluster and high-performance computing.

  • 2009 IEEE International Conference on Cluster Computing (CLUSTER)

    Cluster 2009 welcomes paper and poster submissions on innovative work from researchers in academia, industry, and government, describing original research in the field of cluster computing.


2013 8th International Conference on Computer Engineering & Systems (ICCES)

This conference is the 9th of its series, it aims at gathering academia and industry to present the latest research in computer engineering and systems.

  • 2012 Seventh International Conference on Computer Engineering & Systems (ICCES)

    his conference gathers universities and research centers to present the latest findings in computer engineering and systems.

  • 2011 International Conference on Computer Engineering & Systems (ICCES)

    This conference gathers universities and research centers to present the latest findings in computer engineering and systems.

  • 2010 International Conference on Computer Engineering & Systems (ICCES)

    1. Computer Architecture and Computer Aided Design 2. Embedded Systems & HW/SW Co-Design 3. Networks on Chip, Systems on Chip 4. Computer Networks and Security 5. Mobile and Ubiquitous Computing 6. Quantum Computing and Information 7. Software and Web Engineering 8. Multimedia and Web Applications 9. Data Base and Data Mining 10. Signal Processing 11. Modeling and Simulation 12. Control Systems and Robotics 13. Artificial Intelligence and Evolutionary Computing 14. Reliability and Fault Tole

  • 2009 International Conference on Computer Engineering & Systems (ICCES)

    The aim of this conference is to gather the researchers from academia and industry in Computer Engineering to discuss the recent developments and progress in this field. Conference tracks are: 1. Computer Architecture and Computer Aided Design 2. Embedded Systems & HW/SW Co-Design 3. Networks on Chip, Systems on Chip 4. Computer Networks and Security 5. Mobile and Ubiquitous Computing 6. Quantum Computing and Information 7. Software and Web Engineering 8. Multimedia and Web Applications 9. Datab


2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

2013 IEEE Symposium on Computational Intelligence and Data Mining (IEEE CIDM 2013) will bring together scientists, engineers and students from around the world to discuss the latest advances in the field of computational intelligence applied to issues in data and process mining. This conference will provide a forum for the presentation of recent results in data mining algorithms, applications, software and data and process mining systems.


2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications (ISPA)

ISPA-12 follows the traditions of previous successful ISPA conferences, ISPA-03 (Aizu, Japan), ISPA-04 (Hong Kong), ISPA-05 (Nanjing, China), ISPA-06 (Sorrento, Italy), ISPA-07 (Niagara Falls, Canada) and ISPA-08 (Sydney, Australia), ISPA-09 (Chengdu, China), ISPA-10 (Taipei, Taiwan), ISPA-11 (Busan, Korea). The objective of ISPA 2012 is to provide a forum for scientists and engineers in academia and industry to exchange and discuss their experiences, new ideas, research results, and applications about all aspects of parallel and distributed computing and networking. It will feature session presentations, workshops, tutorials, and keynote speeches. ISPA-12 is sponsored by IEEE Technical Committee on Scalable Computing (TCSC) and IEEE Computer Society.

  • 2011 IEEE 9th International Symposium on Parallel and Distributed Processing with Applications (ISPA)

    Virtualization techniques, tools, and applications; Computer networks; Network routing and communication algorithms; Parallel/distributed system architectures; Tools and environments for software development; Parallel/distributed algorithms; Distributed systems and applications; Wireless networks, mobile and pervasive computing; Reliability, fault-tolerance, and security; Performance evaluation and measurements; Grid and cluster computing; Internet computing and web services; Database applicatio

  • 2010 International Symposium on Parallel and Distributed Processing with Applications (ISPA)

    ISPA 2010 is the 8th in this series of conferences started in 1993 that are devoted to algorithms and architectures for parallel and distributed processing with applications. ISPA 2010 is now recognized as the main regular event of the world that is covering the many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental projects, and commercial components and systems. As applications of computing systems have permeated in every aspects o


2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)

The annual IEEE International Conference on Bioinformatics and Bioengineering covers complementary disciplines that hold great promise for the advancement of research and development in complex medical and biological systems, agriculture, environment, public health, drug design, and so on.

  • 2011 IEEE 11th International Conference on Bioinformatics & Bioengineering (BIBE)

    The annual IEEE International Conference on Bioinformatics and Bioengineering aims at building synergy between Bioinformatics and Bioengineering, two complementary disciplines that hold great promise for the advancement of research and development in complex medical and biological systems, agriculture, environment, public health, drug design.

  • 2010 International Conference on BioInformatics and BioEngineering (BIBE)

  • 2009 9th IEEE International Conference on BioInformatics and BioEngineering - BIBE

    The annual IEEE International Conference on Bioinformatics and Bioengineering aims at building synergy between Bioinformatics and Bioengineering, two complementary disciplines that hold great promise for the advancement of research and development in complex medical and biological systems, agriculture, environment, public health, drug design. Research and development in these two areas are impacting the science and technology in fields such as medicine, food production, forensics, etc.

  • 2008 8th IEEE International Conference on BioInformatics and BioEngineering - BIBE

    The annual IEEE International Conference on Bioinformatics and Bioengineering aims at building synergy between Bioinformatics and Bioengineering, two complementary disciplines that hold great promise for the advancement of research and development in complex medical and biological systems, agriculture, environment, public health, drug design.

  • 2007 7th IEEE International Conference on BioInformatics and BioEngineering - BIBE

    Bioinformatics and Bioengineering are complementary disciplines that hold great promise for the advancement of research and development in complex medical and biological systems, agriculture, environment, public health, drug design, and so on. Research and development in these two areas are impacting the science and technology of fields such as medicine, food production, forensics, etc. by advancing fundamental concepts in molecular biology and in medicine ,by helping us understand living organisms.


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Periodicals related to Clustering algorithms

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Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


Fuzzy Systems, IEEE Transactions on

Theory and application of fuzzy systems with emphasis on engineering systems and scientific applications. (6) (IEEE Guide for Authors) Representative applications areas include:fuzzy estimation, prediction and control; approximate reasoning; intelligent systems design; machine learning; image processing and machine vision;pattern recognition, fuzzy neurocomputing; electronic and photonic implementation; medical computing applications; robotics and motion control; constraint propagation and optimization; civil, chemical and ...


Pattern Analysis and Machine Intelligence, IEEE Transactions on

Statistical and structural pattern recognition; image analysis; computational models of vision; computer vision systems; enhancement, restoration, segmentation, feature extraction, shape and texture analysis; applications of pattern analysis in medicine, industry, government, and the arts and sciences; artificial intelligence, knowledge representation, logical and probabilistic inference, learning, speech recognition, character and text recognition, syntactic and semantic processing, understanding natural language, expert systems, ...


Software Engineering, IEEE Transactions on

Specification, development, management, test, maintenance, and documentation of computer software.


Systems, Man, and Cybernetics, Part B, IEEE Transactions on

The scope of the IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or between machines, humans, and organizations. The scope of Part B includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, ...



Most published Xplore authors for Clustering algorithms

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Xplore Articles related to Clustering algorithms

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DOA Estimation of multipath clusters in WiMedia UWB systems

Ashok Kumar Marath; A. Rahim Leyman; Hari Krishna Garg 2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop, 2008

Ultra wideband (UWB) systems are expected to find widespread use in future short range applications. Increasing popularity of these devices will require management of occupied spectrum in spatial domain to keep the interference low. In UWB systems, one encounters many multipath components. By optimally forming beams in the direction of the principal multipath clusters, one can achieve optimum spectrum efficiency. ...


Nuclei-Based Features for Uterine Cervical Cancer Histology Image Analysis with Fusion-based Classification

Peng Guo; Koyel Banerjee; R. Stanley; Rodney Long; Sameer Antani; George Thoma; Rosemary Zuna; Shellaine Frazier; Randy Moss; William Stoecker IEEE Journal of Biomedical and Health Informatics, 2015

Cervical cancer, which has been affecting women worldwide as the second most common cancer, can be cured if detected early and treated well. Routinely, expert pathologists visually examine histology slides for cervix tissue abnormality assessment. In previous research, we investigated an automated, localized, fusion-based approach for classifying squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia ...


Quantitative assessment of the infarct transmurality using delayed contrast enhanced magnetic resonance images: description and validation

N. Kachenoura; A. Redheuil; R. El-Berbari; C. R. Dominguez; A. Herment; E. Mousseaux; F. Frouin Computers in Cardiology, 2005, 2005

The extent and degree of myocardial injury after an ischemic event are strong predictors of patient's outcome. After acute infarction, delayed contrast enhancement magnetic resonance imaging allows clinicians to distinguish between viable and non-viable myocardium and can delineate with high precision the infarcted tissue. The aim of this study is to provide a quantitative method based on the fuzzy c-means ...


How Many Clusters: A Validation Index for Arbitrary-Shaped Clusters

Ariel E. Bayá ; Pablo M. Granitto IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2013

Clustering validation indexes are intended to assess the goodness of clustering results. Many methods used to estimate the number of clusters rely on a validation index as a key element to find the correct answer. This paper presents a new validation index based on graph concepts, which has been designed to find arbitrary shaped clusters by exploiting the spatial layout ...


A Radial Basis Function Neural Network Classifier for the Discrimination of Individual Odor Using Responses of Thick-Film Tin-Oxide Sensors

Ravi Kumar; R. R. Das; V. N. Mishra; R. Dwivedi IEEE Sensors Journal, 2009

This paper presents a novel approach to odor discrimination of alcohols and alcoholic beverages using published data obtained from the responses of thick film tin oxide sensor array fabricated at our laboratory and employing a combination of transformed cluster analysis and radial basis function neural network. The performance of the new classifier was compared with others based on backpropagation (BP) ...


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Educational Resources on Clustering algorithms

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eLearning

DOA Estimation of multipath clusters in WiMedia UWB systems

Ashok Kumar Marath; A. Rahim Leyman; Hari Krishna Garg 2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop, 2008

Ultra wideband (UWB) systems are expected to find widespread use in future short range applications. Increasing popularity of these devices will require management of occupied spectrum in spatial domain to keep the interference low. In UWB systems, one encounters many multipath components. By optimally forming beams in the direction of the principal multipath clusters, one can achieve optimum spectrum efficiency. ...


Nuclei-Based Features for Uterine Cervical Cancer Histology Image Analysis with Fusion-based Classification

Peng Guo; Koyel Banerjee; R. Stanley; Rodney Long; Sameer Antani; George Thoma; Rosemary Zuna; Shellaine Frazier; Randy Moss; William Stoecker IEEE Journal of Biomedical and Health Informatics, 2015

Cervical cancer, which has been affecting women worldwide as the second most common cancer, can be cured if detected early and treated well. Routinely, expert pathologists visually examine histology slides for cervix tissue abnormality assessment. In previous research, we investigated an automated, localized, fusion-based approach for classifying squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia ...


Quantitative assessment of the infarct transmurality using delayed contrast enhanced magnetic resonance images: description and validation

N. Kachenoura; A. Redheuil; R. El-Berbari; C. R. Dominguez; A. Herment; E. Mousseaux; F. Frouin Computers in Cardiology, 2005, 2005

The extent and degree of myocardial injury after an ischemic event are strong predictors of patient's outcome. After acute infarction, delayed contrast enhancement magnetic resonance imaging allows clinicians to distinguish between viable and non-viable myocardium and can delineate with high precision the infarcted tissue. The aim of this study is to provide a quantitative method based on the fuzzy c-means ...


How Many Clusters: A Validation Index for Arbitrary-Shaped Clusters

Ariel E. Bayá ; Pablo M. Granitto IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2013

Clustering validation indexes are intended to assess the goodness of clustering results. Many methods used to estimate the number of clusters rely on a validation index as a key element to find the correct answer. This paper presents a new validation index based on graph concepts, which has been designed to find arbitrary shaped clusters by exploiting the spatial layout ...


A Radial Basis Function Neural Network Classifier for the Discrimination of Individual Odor Using Responses of Thick-Film Tin-Oxide Sensors

Ravi Kumar; R. R. Das; V. N. Mishra; R. Dwivedi IEEE Sensors Journal, 2009

This paper presents a novel approach to odor discrimination of alcohols and alcoholic beverages using published data obtained from the responses of thick film tin oxide sensor array fabricated at our laboratory and employing a combination of transformed cluster analysis and radial basis function neural network. The performance of the new classifier was compared with others based on backpropagation (BP) ...


More eLearning Resources

IEEE-USA E-Books

  • Data Mining Algorithms I: Clustering

    Clustering is the process of grouping together objects that are similar. The similarity between objects is evaluated by using a several types of dissimilarities (particularly, metrics and ultrametrics). After discussing partitions and dissimilarities, two basic mathematical concepts important for clustering, we focus on ultrametric spaces that play a vital role in hierarchical clustering. Several types of agglomerative hierarchical clustering are examined with special attention to the single-link and complete link clusterings. Among the nonhierarchical algorithms we present the k-means and the PAM algorithm. The well-known impossibility theorem of Kleinberg is included in order to illustrate the limitations of clustering algorithms. Finally, modalities of evaluating clustering quality are examined.

  • Protein Local Structure Prediction

    Studying the protein sequence-structure relationship is one of the most active bioinformatics research areas. A better understanding of the protein sequence- structure correlation can improve effectiveness and efficiency of local protein structure prediction. For the structural-cluster approach, protein structural segments are grouped into different structural clusters using multiple structural alignments and unsupervised clustering algorithms. The support vector machine (SVM) has shown superior classification performance in various bioinformatics applications because of its strong generalization capability. In order to overcome disadvantages of building one SVM over the whole sample space, the clustering support vector machine (CSVM) was proposed to predict distance matrices, torsion angles, and secondary structures for backbone -carbon atoms of protein sequence segments. The special features of CSVMs enable the training tasks for each CSVM to be parallelized. Parallel training processes can increase the speed for the data mining task for very large datasets.

  • Network Algorithms for Protein Interactions

    The task considered in this chapter is to cluster the set of proteins into natural groups that operate together. These groups should be functional groups and can often be identified as serving a particular purpose in the life of a cell. The chapter looks at algorithms for clustering that use optimization methods, often combined with hierarchical methods, as exemplified by the author's work and the work of others. The chapter presents the details of hierarchical algorithms and their performance. Graph theory is commonly used as a method for analyzing protein-protein interactions (PPIs) in computational biology. Each vertex represents a protein, and edges correspond to experimentally identified PPIs. The hierarchical methods discussed here have three main phases: aggregation phase, clustering phase, and disaggregation phase. While there are different coarsening, decoarsening, and clustering algorithms, this framework of optimization-directed hierarchical methods has found success.

  • Partitional Clustering

    This chapter contains sections titled: Introduction Clustering Criteria K-Means Algorithm Mixture Density-Based Clustering Graph Theory-Based Clustering Fuzzy Clustering Search Techniques-Based Clustering Algorithms Applications Summary

  • Clustering Functionally Similar Genes from Microarray Data

    This chapter deals with the application of different rough-fuzzy clustering algorithms for clustering functionally similar genes from microarray gene expression data sets. The effectiveness of the algorithms, along with a comparison with other related gene clustering algorithms, is demonstrated on a set of microarray gene expression data sets using some standard validity indices. The chapter first reports a brief overview of different gene clustering algorithms. It then describes several quantitative and qualitative performance measures such as Silhouette index, Eisen and cluster profile plots, Z score, gene-ontology-based analysis to evaluate the quality of gene clusters. The chapter presents a brief description of different microarray gene expression data sets such as fifteen yeast data, yeast sporulation, Auble data, Cho et al. data, and reduced cell cycle data. It also presents implementation details, experimental results, and a comparison among different algorithms. fuzzy set theory; pattern clustering; performance evaluation; rough set theory

  • Descriptive Modeling

    This chapter contains sections titled: 9.1 Introduction, 9.2 Describing Data by Probability Distributions and Densities, 9.3 Background on Cluster Analysis, 9.4 Partition-Based Clustering Algorithms, 9.5 Hierarchical Clustering, 9.6 Probabilistic Model-Based Clustering Using Mixture Models, 9.7 Further Reading

  • Unsupervised Learning

    This chapter contains sections titled: 7.1 Introduction, 7.2 K-Means Clustering, 7.3 Topology-Preserving Maps, 7.4 Art1, 7.5 Art2, 7.6 Using Clustering Algorithms for Supervised Learning, 7.7 Exercises, 7.8 Programming Projects

  • Node Clustering

    This chapter contains sections titled: Introduction Node Clustering Algorithms Node Clustering Algorithms for Wireless Sensor Networks Summary and Future Directions References

  • Rough-Fuzzy Clustering: Generalized A-Means Algorithm

    Clustering techniques have been effectively applied to a wide range of engineering and scientific disciplines such as pattern recognition, biology, and remote sensing. A number of clustering algorithms have been proposed to suit different requirements. One of the widely used prototype-based partitional clustering algorithms is hard c-means (HCM). This chapter first briefly introduces the necessary notions of HCM, fuzzy c-means (FCM), and rough c-means (RCM) algorithms. It then describes the rough-fuzzy- possibilistic c-means (RFPCM) algorithm in detail on the basis of the theory of rough sets and FCM. The chapter also presents a mathematical analysis of the convergence property of the RFPCM algorithm. It establishes that the RFPCM algorithm is the generalization of existing c-means algorithms. The chapter reports several quantitative performance measures to evaluate the quality of different algorithms. Finally, it presents a few case studies and an extensive comparison with other methods such as crisp, fuzzy, possibilistic, and RCM. fuzzy logic; pattern clustering; performance evaluation; rough set theory

  • No title

    The last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster center, in a dictionary-based representation, a data point can be associated with a small set of dictionary atoms. Thus, dictionary learning provides a more flexible representation of data and may have the potential to capture more relevant features from the original feature space of the data. One of the early algorithms for dictionary learning is K-SVD. In recent years, many variations/extensions of K-SVD and other new algorithms have been proposed, with some aiming at adding discriminative capability to the dictionary, and some attempting to model the relationship of multiple dictionaries. One prominent application of dictionary learning is in the general field of visual computing, where long-standing challenges have seen promising new solutions based on sparse representation with learned dictionaries. With a timely review of recent advances of dictionary learning in visual computing, covering the most recent literature with an emphasis on papers after 2008, this book provides a systematic presentation of the general methodologies, specific algorithms, and examples of applications for those who wish to have a quick start on this subject.



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