Special Session - Clustering and Co-clustering (CluCo)
held at IJCNN 2015

Accepted Papers

Monday, July 13, 9:10AM-10:30AM, Room: Ballroom, Chair: Lemaire, Vincent; Gisbrecht, Andrej; Jean-Charles Lamirel

  • 9:10AM Discriminative Dimensionality Reduction for Regression Problems using the Fisher Metric [#15656]
    Alexander Schulz and Barbara Hammer

  • 9:30AM Automatic Discovery of Metagenomic Structure [#15327]
    Markus Lux, Alexander Sczyrba and Barbara Hammer

  • 9:50AM An Initialization Scheme for Supervized K-means [#15404]
    Vincent Lemaire, Oumaima Alaoui Ismaili and Antoine Cornuejols

  • 10:10AM A New Approach for Event Detection using k-means Clustering and Neural Networks [#15677]
    Muyiwa Olakanmi Oladimeji, Mikdam Turkey, Mohammad Ghavami and Sandra Dudley

  • [CFP...] Everyday, huge amounts of data are generated by users via the web, social networks, etc. Clustering/Coclustering techniques are a tool of choice to help organize the huge collections of data that increasingly beset us. Clustering/Coclustering is an unsupervised learning approach that allows one to discover global structures in the data (i.e. clusters). Given a dataset, it identifies different data subsets which are hopefully meaningful. The discovered clusters are deemed interesting if they are while instances within each (co)cluster share similar features. This (co)clustering problem has motivated a huge body of work and has resulted in a large number of algorithms. (Co)Clustering has thus been used in numerous real-life application domains such as marketing, city planning, and so forth.

    Clustering algorithms are a tool of choice to explore these high-dimensional data sets. However numerous questions remain open as:
    This special session offers a meeting opportunity for academics and industry researchers belonging to the communities of Computational Intelligence, Machine Learning, Experimental Design, and Data Mining to discuss new areas of (co)clustering, One goal of this special session will be two-fold: On the one hand, to look for new algorithms and techniques proposals based on (co)clustering. On the other hand, to look for new application domains, real problems, where the application of (co)clustering have demonstrated an outstanding performance or interpretation abilities against other traditional approaches. Publication opportunities: Papers should be submitted to IJCNN. We encourage papers that describe new algorithm and applications of (co)clustering in real-world. In the industrial context, the main difficulties met and the original solutions developed, have to be described. Paper acceptance and publication will be judged on the basis of their quality and relevance to the special session themes, clarity of presentation, originality and accuracy of results and proposed solutions.
    The set of proposed topics includes, but is not limited to:: The list of application domain is includes, but it is not limited to: A list of Applicative domains could be found in P. Berkhin « Survey of clustering data mining techniques », Accrue Software, San Jose, CA, 2002.