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5 results on '"Cluster labeling"'

1. Bringing a Feature Selection Metric from Machine Learning to Complex Networks

Author

Jean-Charles Lamirel, Anthony Perez, Nicolas Dugué, Laboratoire d'Informatique de l'Université du Mans (LIUM), Le Mans Université (UM), Natural Language Processing : representations, inference and semantics (SYNALP), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire d'Informatique Fondamentale d'Orléans (LIFO), Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université d'Orléans (UO), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Graphes, Algorithmes et Modèles de Calcul (GAMoC), Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, business.industry, Node (networking), [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Feature selection, 02 engineering and technology, Complex network, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Machine learning, computer.software_genre, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020204 information systems, Cluster labeling, Metric (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), 020201 artificial intelligence & image processing, Artificial intelligence, business, Centrality, Cluster analysis, computer, ComputingMilieux_MISCELLANEOUS
Abstract

International audience; Introduced in the context of machine learning, the Feature F-measure is a statistical feature selection metric without parameters that allows to describe classes through a set of salient features. It was shown efficient for classification, cluster labeling and clustering model quality measurement. In this paper, we introduce the Node F-measure, its transposition in the context of networks, where it can by analogy be applied to detect salient nodes in communities. This approach benefits from the parameter-free system of Feature F-Measure, its low computational complexity and its well-evaluated performance. Interestingly, we show that in addition to these properties, Node F-measure is correlated with certain centrality measures, and with measures designed to characterize the community roles of nodes. We also observe that the usual community roles measures are strongly dependent from the size of the communities whereas the ones we propose are by definition linked to the density of the community. This hence makes their results comparable from one network to another. Finally, the parameter-free selection process applied to nodes allows for a universal system, contrary to the thresholds previously defined empirically for the establishment of community roles. These results may have applications regarding leadership in scientific communities or when considering temporal monitoring of communities.

Published
2018
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2. Towards Ontology Reasoning for Topological Cluster Labeling

Author

Isabelle Mougenot, Laure Berti-Equille, Younès Bennani, Hatim Chahdi, Nistor Grozavu, Laboratoire d'Informatique de Paris-Nord (LIPN), Université Sorbonne Paris Cité (USPC)-Institut Galilée-Université Paris 13 (UP13)-Centre National de la Recherche Scientifique (CNRS), UMR 228 Espace-Dev, Espace pour le développement, Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Montpellier (UM)-Université de Guyane (UG)-Université des Antilles (UA), Qatar Computing Research Institute [Doha, Qatar] (QCRI), ANR-12-MONU-0001,Coclico,COllaboration, CLassification, Incrémentalité et COnnaissances(2012), Université Paris 13 (UP13)-Institut Galilée-Université Sorbonne Paris Cité (USPC)-Centre National de la Recherche Scientifique (CNRS), and Université de Guyane (UG)-Université des Antilles (UA)-Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Montpellier (UM)

Subjects
Knowledge representation and reasoning, Computer science, Reasoning & Knowledge Representation, Ontologie, 02 engineering and technology, Ontology (information science), Topology, Machine learning, computer.software_genre, Semantics, Logique de Description, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Semantic Analysis, Description logic, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Cluster labelling, Cluster analysis, Reasoning system, business.industry, Ontology, Description logic reasoning, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Self organising maps SOM, ComputingMethodologies_PATTERNRECOGNITION, Raisonnement, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Cluster labeling, Unsupervised learning, 020201 artificial intelligence & image processing, Artificial intelligence, Data mining, business, computer
Abstract

International audience; In this paper, we present a new approach combining topological un-supervised learning with ontology based reasoning to achieve both : (i) automatic interpretation of clustering, and (ii) scaling ontology reasoning over large datasets. The interest of such approach holds on the use of expert knowledge to automate cluster labeling and gives them high level semantics that meets the user interest. The proposed approach is based on two steps. The first step performs a topographic unsupervised learning based on the SOM (Self-Organizing Maps) algorithm. The second step integrates expert knowledge in the map using ontol-ogy reasoning over the prototypes and provides an automatic interpretation of the clusters. We apply our approach to the real problem of satellite image classification. The experiments highlight the capacity of our approach to obtain a semantically labeled topographic map and the obtained results show very promising performances.

Published
2016
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3. Computational methods for evaluation of cell-based data assessment—Bioconductor

Author

Nolwenn Le Meur, Physiopathologie et pharmacologie cellulaires et moléculaires, and Université de Nantes (UN)-IFR26-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Quality Control, Standardization, Computer science, Data management, Statistics as Topic, Biomedical Engineering, Bioengineering, Bioinformatics, computer.software_genre, Bioconductor, 03 medical and health sciences, Automation, 0302 clinical medicine, Artificial Intelligence, Cluster Analysis, Cluster analysis, 030304 developmental biology, 0303 health sciences, business.industry, Computational Biology, Reproducibility of Results, Flow Cytometry, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], High-Throughput Screening Assays, Workflow, Cluster labeling, Anomaly detection, Data mining, business, computer, 030215 immunology, Biotechnology
Abstract

International audience; Recent advances in miniaturization and automation of technologies have enabled cell-based assay high-throughput screening, bringing along new challenges in data analysis. Automation, standardization, reproducibility have become requirements for qualitative research. The Bioconductor community has worked in that direction proposing several R packages to handle high-throughput data including flow cytometry (FCM) experiment. Altogether, these packages cover the main steps of a FCM analysis workflow, that is, data management, quality assessment, normalization, outlier detection, automated gating, cluster labeling, and feature extraction. Additionally, the open-source philosophy of R and Bioconductor, which offers room for new development, continuously drives research and improvement of theses analysis methods, especially in the field of clustering and data mining. This review presents the principal FCM packages currently available in R and Bioconductor, their advantages and their limits.

Published
2013
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4. Variations to incremental growing neural gas algorithm based on label maximization

Author

Ghada Safi, Pascal Cuxac, Raghvendra Mall, Jean-Charles Lamirel, Natural Language Processing: representation, inference and semantics (TALARIS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), International Institute of Information Technology [Hyperabad] (IIIT-H), Institut de l'information scientifique et technique (INIST), Centre National de la Recherche Scientifique (CNRS), Computer Science Department, University of Pennsylvania, University of Pennsylvania [Philadelphia], and International Institute of Information Technology Hyperabad (IIIT-H)

Subjects
Clustering high-dimensional data, Neural gas, Fuzzy clustering, Clustering algorithms, Computer science, Correlation clustering, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], 050905 science studies, computer.software_genre, CURE data clustering algorithm, Labeling, 0202 electrical engineering, electronic engineering, information engineering, Algorithm design and analysis, Prototypes, Cluster analysis, k-medians clustering, ComputingMilieux_MISCELLANEOUS, Neurons, business.industry, 05 social sciences, Pattern recognition, Size measurement, ComputingMethodologies_PATTERNRECOGNITION, Data stream clustering, Embryo, Cluster labeling, Canopy clustering algorithm, FLAME clustering, 020201 artificial intelligence & image processing, Data mining, Artificial intelligence, 0509 other social sciences, business, Algorithm, computer
Abstract

International audience; Neural clustering algorithms show high performance in the general context of the analysis of homogeneous textual dataset. This is especially true for the recent adaptive versions of these algorithms, like the incremental growing neural gas algorithm (IGNG) and the labeling maximization based incremental growing neural gas algorithm (IGNG-F). In this paper we highlight that there is a drastic decrease of performance of these algorithms, as well as the one of more classical algorithms, when a heterogeneous textual dataset is considered as an input. Specific quality measures and cluster labeling techniques that are independent of the clustering method are used for the precise performance evaluation. We provide new variations to incremental growing neural gas algorithm exploiting in an incremental way knowledge from clusters about their current labeling along with cluster distance measure data. This solution leads to significant gain in performance for all types of datasets, especially for the clustering of complex heterogeneous textual data.

Published
2011
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5. The Structure and Dynamics of Co Citation Clusters: A Multiple Perspective Co-Citation Analysis

Author

Chaomei Chen, Fidelia Ibekwe-SanJuan, Jianhua Hou, College of Information Science and Technology, Drexel University, Drexel University, Equipe de recherche de Lyon en sciences de l'information et de la communication (ELICO), Sciences Po Lyon - Institut d'études politiques de Lyon (IEP Lyon), Université de Lyon-Université de Lyon-École nationale supérieure des sciences de l'information et des bibliothèques (ENSSIB), Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université Lumière - Lyon 2 (UL2), WISELAB, Dalian University of Technology, Dalian, China, University of Technology, Dalian, Université Lumière - Lyon 2 (UL2)-École nationale supérieure des sciences de l'information et des bibliothèques (ENSSIB), Université de Lyon-Université de Lyon-Sciences Po Lyon - Institut d'études politiques de Lyon (IEP Lyon), Université de Lyon, and Dalian University of Technology

Subjects
FOS: Computer and information sciences, Computer Networks and Communications, Computer science, [SHS.INFO]Humanities and Social Sciences/Library and information sciences, A multiple-perspective co-citation analysis, computer.software_genre, Co-citation, Computer Science - Computers and Society, Artificial Intelligence, Graph drawing, Computers and Society (cs.CY), 0502 economics and business, Cluster (physics), GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), interpretation, visualization, Interpretability, 05 social sciences, summarization, methodology, Automatic summarization, Spectral clustering, Visualization, Human-Computer Interaction, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Cluster labeling, Data mining, 0509 other social sciences, 050904 information & library sciences, computer, 050203 business & management, Software, Information Systems
Abstract

A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Co-citation networks are decomposed into co-citation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a co-citation cluster's members and their citers. The generic method is applied to a three-part analysis of the field of Information Science as defined by 12 journals published between 1996 and 2008: 1) a comparative author co-citation analysis (ACA), 2) a progressive ACA of a time series of co-citation networks, and 3) a progressive document co-citation analysis (DCA). Results show that the multiple-perspective method increases the interpretability and accountability of both ACA and DCA networks., Comment: 33 pages, 11 figures, 10 tables. To appear in the Journal of the American Society for Information Science and Technology

Published
2010
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