• A Cluster Analysis of Stock Market Data Using Hierarchical SOMs 

      Astudillo, César A.; Poblete, Jorge; Resta, Marina; Oommen, John (Chapter; Peer reviewed, 2016)
      The analysis of stock markets has become relevant mainly because of its financial implications. In this paper, we propose a novel methodology for performing a structured cluster analysis of stock market data. Our proposed ...
    • Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers 

      Astudillo, César A.; Gonzalez, Javier I.; Oommen, John; Yazidi, Anis (Chapter, 2016)
      In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called ...
    • Imposing tree-based topologies onto self organizing maps 

      Astudillo, César A.; Oommen, B. John (Journal article; Peer reviewed, 2011)
      The beauty of the Kohonen map is that it has the property of organizing the codebook vectors, which represent the data points, both with respect to the underlying distribution and topologically. This topology is traditionally ...
    • Pattern Recognition using the TTOCONROT 

      Astudillo, César A.; Oommen, John (Chapter; Peer reviewed, 2015)
    • Semi-supervised classification using tree-based self-organizing maps 

      Astudillo, César A.; Oommen, B. John (Lecture Notes in Computer Science;7106, Chapter; Peer reviewed, 2011)
      This paper presents a classifier which uses a tree-based Neural Network (NN), and uses both, unlabeled and labeled instances. First, we learn the structure of the data distribution in an unsupervised manner. After convergence, ...