Archivi tag: clustering
Co-organized Mini-Symposium: MultiClust at SIAM DM 2014
Andrea is Co-Organizer of the upcoming SIAM Data Mining Symposium on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering (MultiClust)
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Contrassegnato clustering, clustering ensembles, co-clustering, projective clustering
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Metacluster-based Projective Clustering Ensembles
F. Gullo, C. Domeniconi, A. Tagarelli. Metacluster-based Projective Clustering Ensembles. Machine Learning Journal (SI on MultiClust), Springer. Accepted: June 7, 2013, Online First: July 2, 2013. DOI: 10.1007/s10994-013-5395-y
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Contrassegnato clustering, clustering ensembles, dimensionality reduction, multi-view clustering, projective clustering, subspace clustering
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Co-organized Workshop: MultiClust Workshop at ACM SIGKDD 2013
Andrea is Co-Organizer of the upcoming ACM SIGKDD 2013 Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering (MultiClust)
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Contrassegnato alternative clustering, clustering, clustering ensembles, knowledge-driven clustering, multi-source clustering, subspace clustering
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Minimizing the variance of cluster mixture models for clustering uncertain objects
F. Gullo, G. Ponti, A. Tagarelli. Minimizing the variance of cluster mixture models for clustering uncertain objects. Statistical Analysis and Data Mining (SAM) 6(2):116-135, 2013. Online First: November 19, 2012.
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Contrassegnato clustering, uncertain cluster prototype, uncertain data
Commenti disabilitati su Minimizing the variance of cluster mixture models for clustering uncertain objects
Multiobjective Optimization of Co-Clustering Ensembles
F. Gullo, AKM Khaled Ahsan Talukder, S. Luke, C. Domeniconi, A. Tagarelli. Multiobjective Optimization of Co-Clustering Ensembles. Fourteenth International Conference on Genetic and Evolutionary Computation (GECCO), pp. 1495-1496. Philadelphia, USA, July 7-11, 2012.
Pubblicato in Conference Proceedings, Publications
Contrassegnato clustering, clustering ensembles, co-clustering, optimization
Commenti disabilitati su Multiobjective Optimization of Co-Clustering Ensembles
Projective Clustering Ensembles
F. Gullo, C. Domeniconi, A. Tagarelli. Projective Clustering Ensembles. Data Mining and Knowledge Discovery (DAMI), Accepted April 3, 2012, Online First: May 3, 2012.
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Contrassegnato clustering, clustering ensembles, optimization, projective clustering
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Uncertain Centroid based Partitional Clustering of Uncertain Data
F. Gullo, A. Tagarelli. Uncertain Centroid based Partitional Clustering of Uncertain Data. Proceedings of the VLDB Endowment (ACM), 5(7):610-621, 2012. PDF
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Contrassegnato clustering, uncertain data
Commenti disabilitati su Uncertain Centroid based Partitional Clustering of Uncertain Data
Co-organized Workshop: 3Clust Workshop at PAKDD 2012
Andrea is Co-Organizer of the upcoming PAKDD 2012 Workshop on Multi-view data, High-dimensionality, External Knowledge: Striving for a Unified Approach to Clustering (3Clust)
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Contrassegnato alternative clustering, clustering, clustering ensembles, subspace clustering
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A Time Series Approach for Clustering Mass Spectrometry Data
F. Gullo, G. Ponti, A. Tagarelli, G. Tradigo, P. Veltri. A Time Series Approach for Clustering Mass Spectrometry Data. Journal of Computational Science, 3:344-355, 2012. Accepted for publication: June 30, 2011. Available on-line: July 12, 2011
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Contrassegnato clinical data, clustering, mass spectrometry, proteomics, time series
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Edited Book: XML Data Mining
Andrea is Editor of the upcoming book “XML Data Mining: Models, Methods, and Applications”, IGI Global, 538 pages. Copyright 2012. Release date: November 2011. Publisher’s book page Book Brochure Book Brochure with TOC
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Contrassegnato association rule extraction, classification, clustering, document clustering, frequent pattern discovery, Semantic Web, XML content clustering, XML mining, XML structure clustering
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Advancing Data Clustering via Projective Clustering Ensembles
F. Gullo, C. Domeniconi, A. Tagarelli. Advancing Data Clustering via Projective Clustering Ensembles. ACM International Conference on Management of Data (SIGMOD’11), pp. 733-744. Athens, Greece, June 12-16, 2011. PDF This paper has been awarded of the SIGMOD11 Repeatability/Workability Evaluation Test. SIGMOD has … Continua a leggere
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Contrassegnato clustering, clustering ensembles, optimization, projective clustering
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Enhancing Single-Objective Projective Clustering Ensembles
F. Gullo, C. Domeniconi, A. Tagarelli. Enhancing Single-Objective Projective Clustering Ensembles. 10th IEEE International Conference on Data Mining (ICDM ’10), pp. 833-838. Sydney, Australia, December 14-17, 2010. PDF
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Contrassegnato clustering, clustering ensembles, optimization, projective clustering
Commenti disabilitati su Enhancing Single-Objective Projective Clustering Ensembles
Minimizing the Variance of Cluster Mixture Models for Clustering Uncertain Objects
F. Gullo, G. Ponti, A. Tagarelli. Minimizing the Variance of Cluster Mixture Models for Clustering Uncertain Objects. 10th IEEE International Conference on Data Mining (ICDM ’10), pp. 839-844. Sydney, Australia, December 14-17, 2010.
Pubblicato in Conference Proceedings
Contrassegnato clustering, uncertain data, variance of mixture models
Commenti disabilitati su Minimizing the Variance of Cluster Mixture Models for Clustering Uncertain Objects
Projective Clustering Ensembles
F. Gullo, C. Domeniconi, A. Tagarelli. Projective Clustering Ensembles. 9th IEEE International Conference on Data Mining (ICDM ’09), pp. 794-799. Miami, Florida, USA, December 6-9, 2009. PDF
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Contrassegnato clustering, clustering ensembles, optimization, projective clustering
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A Time Series Representation Model for Accurate and Fast Similarity Detection
F. Gullo, G. Ponti, A. Tagarelli, S. Greco. A Time Series Representation Model for Accurate and Fast Similarity Detection. Pattern Recognition 42(11):2998-3014, 2009.
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Contrassegnato classification, clustering, DSA, similarity detection, time series
Commenti disabilitati su A Time Series Representation Model for Accurate and Fast Similarity Detection
Low-voltage Electricity Customer Profiling based on Load Data Clustering
F. Gullo, G. Ponti, A. Tagarelli, S. Iiritano, M. Ruffolo, D. Labate. Low-voltage Electricity Customer Profiling based on Load Data Clustering. 13th International Database Engineering and Applications Symposium (IDEAS ’09), pp. 330-333. Cetraro, Italy, September 16-18, 2009.
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Contrassegnato clustering, time series
Commenti disabilitati su Low-voltage Electricity Customer Profiling based on Load Data Clustering
Hierarchical Clustering of Microarray Data with Probe-level Uncertainty
F. Gullo, G. Ponti, A. Tagarelli, G. Tradigo, P. Veltri. Hierarchical Clustering of Microarray Data with Probe-level Uncertainty. 22nd IEEE International Symposium on Computer-Based Medical Systems (CBMS ’09). Albuquerque, New Mexico, USA, August 3-4, 2009. PDF
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Contrassegnato clustering, microarray, uncertain data
Commenti disabilitati su Hierarchical Clustering of Microarray Data with Probe-level Uncertainty
Information-Theoretic Hierarchical Clustering of Uncertain Data
F. Gullo, G. Ponti, A. Tagarelli, S. Greco. Information-Theoretic Hierarchical Clustering of Uncertain Data. 17th Italian Symposium on Advanced Database Systems (SEBD ’09), pp. 273-280. Camogli (Genova), Italy, June 21-24, 2009.
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Contrassegnato clustering, uncertain data
Commenti disabilitati su Information-Theoretic Hierarchical Clustering of Uncertain Data
Diversity-based Weighting Schemes for Clustering Ensembles
F. Gullo, A. Tagarelli, S. Greco. Diversity-based Weighting Schemes for Clustering Ensembles. 9th SIAM International Conference on Data Mining (SDM ’09), pp. 437-448. Sparks, Nevada, USA, April 30-May 2, 2009.
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Contrassegnato clustering, clustering ensembles
Commenti disabilitati su Diversity-based Weighting Schemes for Clustering Ensembles
MaSDA: A System for Analyzing Mass Spectrometry Data
F. Gullo, G. Ponti, A. Tagarelli, G. Tradigo, P. Veltri. MaSDA: A System for Analyzing Mass Spectrometry Data. Computer Methods and Programs in Biomedicine 95(2):S12-21, 2009.
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Contrassegnato clustering, DSA, mass spectrometry, time series
Commenti disabilitati su MaSDA: A System for Analyzing Mass Spectrometry Data
A Hierarchical Algorithm for Clustering Uncertain Data via an Information-Theoretic Approach
F. Gullo, G. Ponti, A. Tagarelli, S. Greco. A Hierarchical Algorithm for Clustering Uncertain Data via an Information-Theoretic Approach. 8th IEEE International Conference on Data Mining (ICDM ’08), pp. 821-826. Pisa, Italy, December 15-19, 2008.
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Contrassegnato clustering, hierarchical clustering, information-theoretic clustering, uncertain data
Commenti disabilitati su A Hierarchical Algorithm for Clustering Uncertain Data via an Information-Theoretic Approach
Clustering Uncertain Data Via K-Medoids
F. Gullo, G. Ponti, A. Tagarelli. Clustering Uncertain Data Via K-Medoids. 2nd International Conference on Scalable Uncertainty Management (SUM ’08), pp. 229-242, LNAI 5291. Naples, Italy, October 1-3, 2008.
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Contrassegnato clustering, medoids, uncertain data
Commenti disabilitati su Clustering Uncertain Data Via K-Medoids
A Time Series Based Approach for Classifying Mass Spectrometry Data
F. Gullo, G. Ponti, A. Tagarelli, G. Tradigo, P. Veltri. A Time Series Based Approach for Classifying Mass Spectrometry Data. 20th IEEE International Symposium on Computer-Based Medical Systems (CBMS ’07), pp. 412-420. Maribor, Slovenia, June 20-22, 2007.
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Contrassegnato clustering, data reduction, mass spectrometry, time series
Commenti disabilitati su A Time Series Based Approach for Classifying Mass Spectrometry Data
Mining scientific results through the combined use of clustering and linear programming techniques
A. Tagarelli, I. Trubitsyna, S. Greco. Mining scientific results through the combined use of clustering and linear programming techniques. 6th International Conference on Enterprise Information Systems (ICEIS ’04), vol. 2, pp. 84-91. Porto, Portugal, April 14-17, 2004.
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Contrassegnato clustering, Data envelopment analysis, linear programming
Commenti disabilitati su Mining scientific results through the combined use of clustering and linear programming techniques
Combining Linear Programming and Clustering Techniques for the Classification of Research Centers
A. Tagarelli, I. Trubitsyna, S. Greco. Combining Linear Programming and Clustering Techniques for the Classification of Research Centers. The European Journal on Artificial Intelligence, AI Communications 17(3):111-122, 2004.
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Contrassegnato clustering, Data envelopment analysis, linear programming
Commenti disabilitati su Combining Linear Programming and Clustering Techniques for the Classification of Research Centers
Mining Scientific Results to Measure the Efficiency of Research Centers
A. Tagarelli, I. Trubitsyna, A. Mecchia, T. Mostardi, R. Pupo. Mining Scientific Results to Measure the Efficiency of Research Centers. 11th Italian Symposium on Advanced Database Systems (SEBD ’03), pp. 147-160. Cetraro, Italy, June 2003.
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Contrassegnato clustering, linear programming
Commenti disabilitati su Mining Scientific Results to Measure the Efficiency of Research Centers