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Traveller search in Code Vector Activity Detection based GLA

Clustering Methods 2010 Aki Heikkinen. Traveller search in Code Vector Activity Detection based GLA . Fast Exact GLA Based on Code Vector Activity Detection. Centroid are classified into states [2]: Active Static Data point classifications [2]:

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Traveller search in Code Vector Activity Detection based GLA

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  1. Clustering Methods 2010 Aki Heikkinen Travellersearch in CodeVectorActivityDetectionbased GLA

  2. FastExact GLA Based on CodeVectorActivityDetection • Centroidareclassified into states [2]: • Active • Static • Data pointclassifications [2]: • Static, when the centroid is static • Balanced, when the centroid is active, but the distancebetweencentroid and data pointdidn’tchange • Farther, when the centroid is active and itmovesawayfrom the datapoint • Closer, when the centroid is active and itmovescloser to the datapoint

  3. TravellerSearch Algorithmimprovement: When data point is in ’closerstate’ (ie. currentcentroid hasmovedcloser to the data point) instead of searchingall activecentroids, seachonly the currentcentroid and all the other activecentroidsthathavemovedgreaterdistancethan the currentcentroid [1]. Centroidsmovinggreaterdistanceare ”travellers”

  4. TravellerSearch D1 <D5? NO! D6 D2 D1 < D6? ok D1 < D3? ok D1 < D2? ok D1 <D4? NO! D5 Closer data point D1 D3 Search the nearestcentroid! D4

  5. TestResults Specs: Average of 500 runs 100 swaps 2 K-Meaniterations S1-Dataset MSE TIME Default 0,89875 0,48281 Travellersearch0,904560,46938 S2-Dataset MSE TIME Default 1,33087 0,54935 Travellersearch1,329320,52371

  6. TestResults Specs: Average of 100 runs 100 swaps 2 K-Meaniterations Birch1-Dataset MSE TIME Default 4,74680 31,77298 Travellersearch4,7461930,74745

  7. TestResults Specs: Average of 100 runs 100 swaps 20 K-Meaniterations S1-Dataset MSE TIME Default 0,89176 1,41334 Travellersearch 0,891761,40127 S2-Dataset MSE TIME Default 1,32791 2,25996 Travellersearch 1,327912,22452

  8. TestResults Specs: Average of 100 runs 100 swaps 50 K-Meaniterations S1-Dataset MSE TIME Default 0,89176 1,50824 Travellersearch 0,89176 1,49933 S2-Dataset MSE TIME Default 1,32791 2,46833 Travellersearch 1,327912,43418

  9. TestResults Specs: Average of 500 runs K-Meanalgorithm S1-Dataset MSE TIME Default1,87835 0,04246 Travellersearch1,895140,04172 S2-Dataset MSE TIME Default1,984990,05895 Travellersearch1,992120,05600

  10. References [1] Kuo-LiangChung, Jhin-SianLin, Faster and morerobustpointsymmtery-based K-means algorithm, PatternRecognition, 40, 410-422, 2007. [2] T. Kaukoranta, P. Fränti, O. Nevalainen, A fastexact GLA based on codevectoractivitydetection, IEEE Trans. on ImageProcessing, 9 (8), 1337-1342, August 2000.

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