FLAME clustering: Difference between revisions

Revision as of 15:18, 27 November 2007

Revision as of 05:52, 28 November 2007

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'''Fuzzy clustering by Local ApproximationSpproximation of MEmberships (FLAME)''' is a novel [[data clustering]] algorithm that defines clusters in the dense parts of a dataset and perform cluster assignment solely based on the '''neighborhood relationships''' among objects. The key feature of this algorithm is that, the neighborhood relationships among neighboring objects in the feature space are used to constrain the memberships of neighboring objects in the fuzzy membership space.

==Description of the FLAME algorithm==

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## One-to-multiple object-clusters assignment, to assign each object to the cluster in which it has a membership higher than a threshold.

==The Optimizationoptimization Problemproblem in FLAME==

The '''Local/Neighborhood Approximation of Fuzzy Memberships''' is a procedure to minimize the '''Local/Neighborhood Approximation Error (LAE/NAE)''' defined as the following:

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</math>

==A Simplesimple Illustrationillustration on a 2D Testingtesting Datasetdataset==

[[Image:FLAME_Demo.png|800px]]