Artur Abdullin

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This paper tackles the issue of building a large-scale virtual organization for individuals and institutions that are associated with the field of computational intelligence (CI) and machine learning (ML). It begins with a few scenarios that will help illustrate the need for a virtual community in CIML.
Recent years have seen an increasing interest in clustering data comprising multiple domains or modalities, such as categorical, numerical and transactional, etc. This kind of data is sometimes found within the context of clustering multiview, heterogeneous, or multimodal data. Traditionally, different types of attributes or domains have been handled by(More)
We propose a new methodology for clustering data comprising multiple domains or parts, in such a way that the separate domains mutually supervise each other within a semi-supervised learning framework. Unlike existing uses of semi-supervised learning, our methodology does not assume the presence of labels from part of the data, but rather, each of the(More)
We propose a semi-supervised framework to handle diverse data formats or data with mixed-type attributes. Our preliminary results in clustering data with mixed numerical and categorical attributes show that the proposed semi-supervised framework gives better clustering results in the categorical domain. Thus the seeds obtained from clustering the numerical(More)
—In a virtual organization, the interaction of its members for any purpose generates a sequence of activities referred to as a workflow. This paper seeks to identify the work-flows needed for the Computational Intelligence and Machine Learning Virtual Organization. The underlying architecture of the repository should support these workflows in a smooth and(More)
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