Tag Archives: clustering

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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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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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.

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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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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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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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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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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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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 … Continue reading

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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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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.

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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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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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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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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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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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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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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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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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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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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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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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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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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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