Parallel Fast Simulated Annealing Algorithm for Linear ...

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FSA paralellization Current global solution Neighbor 1 Neighbor 2 … Neighbor n cores FSA FSA FSA Current 1 Current 2 Current n cores Make global current either best improving or random non-improving solution 8
Sample size reduction • For each sample element u i a positive weight p i is assigned. It incorporates an information about a relative deformation of the element’s distance to other sample points. Data elements with higher weight could then be treated as more adequate. Weights are normalized to fulfill p i = 1 . • Consequently weights could be then used to improve the performance of data mining procedures e.g. by introducing such weights into the definition of the classic data mining algorithms (e.g. k-means or k-nearest neighbor). • Alternatively one can use weights to eliminate some data elements from the sample. It can be performed by removing from the sample data elements with associated weights fulfilling following condition: p i < P where P ∊ [0, 1] and then normalizing all weights. One can achieve in this way simultaneous dimensionality and sample length reduction with P serving as a data compression ratio. 9
Page 1 and 2: 7th International Conference on Adv
Page 3 and 4: Curse of dimensionality - example S
Page 5 and 6: General description of the algorith
Page 7: FSA temperature and termination cri
Page 11 and 12: Example: seeds dataset (7D→2D) Ou
Page 13 and 14: More details - cluster analysis gla
Page 15 and 16: Thank you for your attention!

Parallel Fast Simulated Annealing Algorithm for Linear ...

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