Publicaciones en las que colabora con JOSE IGNACIO MARTIN ARAMBURU (13)

2011

  1. Towards a standard methodology to evaluate internal cluster validity indices

    Pattern Recognition Letters, Vol. 32, Núm. 3, pp. 505-515

2010

  1. Consolidated trees versus bagging when explanation is required

    Computing (Vienna/New York), Vol. 89, Núm. 3-4, pp. 113-145

  2. Obtaining optimal class distribution for decision trees: Comparative analysis of CTC and C4.5

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  3. SEP/COP: An efficient method to find the best partition in hierarchical clustering based on a new cluster validity index

    Pattern Recognition, Vol. 43, Núm. 10, pp. 3364-3373

2009

  1. SIHC: A STABLE INCREMENTAL HIERARCHICAL CLUSTERING ALGORITHM

    ICEIS 2009 : PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, VOL AIDSS

  2. SIHC: A stable incremental hierarchical clustering algorithm

    ICEIS 2009 - 11th International Conference on Enterprise Information Systems, Proceedings

2008

  1. Evaluation of Malware clustering based on its dynamic behaviour

    Conferences in Research and Practice in Information Technology Series, Vol. 87, pp. 163-170

  2. Service-independent payload analysis to improve intrusion detection in network traffic

    Conferences in Research and Practice in Information Technology Series, Vol. 87, pp. 171-178

2007

  1. CTC: An alternative to extract explanation from bagging

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  2. Combining multiple class distribution modified subsamples in a single tree

    Pattern Recognition Letters, Vol. 28, Núm. 4, pp. 414-422

2006

  1. Consolidated trees: An analysis of structural convergence

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2005

  1. Consolidated tree classifier learning in a car Insurance fraud detection domain with class imbalance

    Lecture Notes in Computer Science

  2. Consolidated trees: Classifiers with stable explanation. A model to achieve the desired stability in explanation

    Lecture Notes in Computer Science