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December 8, 2010

Mtg: From Clusters to Perceptions: The Evolution of Fuzzy Clustering

by @ 3:52 pm. Filed under ALL, Computers/Software, Engineering Mgmt
 

WEDNESDAY December 15, 2010
SCV Computational Intelligence Chapter
Speaker: Dr. Enrique H. Ruspini, Director of the Collaborative Soft Intelligent Systems Laboratory, European Centre for Soft Computing, Spain
Time: Presentation at 6:30 PM
Cost: none
Place: Carnegie Mellon Silicon Valley, Bldg. 23, Room 118, Moffett Field
RSVP: not required
Web: www.e-grid.net/docs/1012-scv-cis.pdf

The ever-increasing amount of data accessible through computer networks has spurred the application of methods for its analysis, summarization, and interpretation. Clustering techniques, in particular, have been employed to discover knowledge and to reduce data complexity. This body of techniques, often described as performing â??numerical classification??? or â??unsupervised pattern recognition,??? encompass a wide diversity of procedures having different computational goals, which are rarely made either explicit or clear by their developers.? Since their inception, clustering approaches based on the theory of fuzzy sets have been extensively applied due to their rich representational capabilities, their formal mathematical underpinnings, and the relations between the nature of fuzzy classifications and utilitarian and metric concepts such as preferences and similarities. Furthermore, the very nature of fuzzy clustering methods readily permits the definition of interesting data structures as instances of paradigmatic models that are approximated by subsets of the dataset being analyzed.
In our presentation, we will review the motivation and evolution of fuzzy-set based methods to discover structures in data. Our point of departure will be the initial proposal for the formulation of relational fuzzy clustering as an optimization problem over the set of all partitions of a subset of a metric space. We will also examine the related problem of partitioning a subset of a vector space and discuss major approaches to its treatment. Continuing our retrospective examination of fuzzy-clustering techniques we will focus on significant milestones in the evolution of this methodology including the generalizations of the notions of prototype, clustering, and fuzzy partition.? The objective of this review is to motivate the introduction of qualitative object description methods, which are soft-computing based approaches for the representation of complex objects in terms of significant qualitative features (e.g., interesting substructures in biological molecules) and by identification of qualitative relationships between those features (e.g., spatial relations between features). We will conclude with a brief discussion of recent applications of QOD methods to important problems in the applied sciences.

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