Milos Jovanovic

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Clustering algorithms are well-established and widely used for solving data-mining tasks. Every clustering algorithm is composed of several solutions for specific sub-problems in the clustering process. These solutions are linked together in a clustering algorithm, and they define the process and the structure of the algorithm. Frequently, many of these(More)
— Typical data mining algorithms follow a so called " black-box " paradigm, where the logic is hidden from the user not to overburden him. We show that " white-box " algorithms constructed with reusable components design can have significant benefits for researchers, and end users as well. We developed a component-based algorithm design platform, and used(More)
We propose a generic decision tree framework that supports reusable components design. The proposed generic decision tree framework consists of several sub-problems which were recognized by analyzing well-known decision tree induc-CTREE. We identified reusable components in these algorithms as well as in several of their partial improvements that can be(More)
This paper presents a contribution to the study of control law structures and to the selection of relevant sensory information for humanoid robots in situations where dynamic balance is jeopardized. In the example considered, the system first experiences a large disturbance, and then by an appropriate control action resumes a " normal " posture of standing(More)
The analysis of microarray data is fundamental to microbiology. Although clustering has long been realized as central to the discovery of gene functions and disease diagnostic, researchers have found the construction of good algorithms a surprisingly difficult task. In this paper, we address this problem by using a component-based approach for clustering(More)
—Several external indices that use information not present in the dataset were shown to be useful for evaluation of representative based clustering algorithms. However, such supervised measures are not directly useful for construction of better clustering algorithms when class labels are not provided. We propose a method for identifying internal cluster(More)
Conditional probabilistic graphical models provide a powerful framework for structured regression in spatio-temporal datasets with complex correlation patterns. However, in real-life applications a large fraction of observations is often missing, which can severely limit the representational power of these models. In this paper we propose a Marginalized(More)