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Object Shape Data Clustering Analysis Based on Time Sequence

This presentation explores clustering algorithms K-means and Agglomerative Clustering applied to object shape data based on time sequence. Results show the effectiveness of different techniques and the impact of prior knowledge on clustering outcomes.

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Object Shape Data Clustering Analysis Based on Time Sequence

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  1. CIS595-Presentation Clustering Analysis of the Object Shape Data Based on Time Sequence Presented by: Wang , Qiang

  2. Agenda • Introduction • Proposed techniques • Experimental results • Conclusion

  3. Introduction • Shape description • Time sequence • Data set: 1400 images ( 70 classes * 20 objects)

  4. Proposed Technique Clustering Algorithms: • K-means clustering • Agglomerative Clustering

  5. Proposed Technique Multidimensional Scaling n n STRESS(X) =  wij(ij-dij)2 i=1 j=1 ij : the reproduced distance ( in new space) dij: the original distance Wij: Weight (to keep Stress falls in [0,1])

  6. Randomly choose the original cluster centers Choose original centers with prior knowledge Over the whole data set(1400 objects) 0.2871 0.3750 Over the subset with good quality(300 objects) 0.9400 1.00 Experimental results K-means Clustering Definition of Correctness: if above 60% objects from a class are clustered together, we say this class is a correctly clustered. Result:

  7. Experimental results Agglomerative Clustering Single: Distance(:, MinI) = min(Distance(:, MinI), Distance(:, MinJ)) Complete:Distance(:, MinI) = max(Distance(:, MinI), Distance(:, MinJ)); Centroid: Distance(:, MinI) = (Distance(:, MinI) + Distance(:, MinJ))/2; ‘Complete’ algorithm has the best experimental result!

  8. Experimental results Agglomerative Clustering Over the whole data set(70 clusters): [1x88 ] [1x130 ] [1x49 ] [1x16 ] [1x75 ] [1x36 ] [1x37 ] [1x30 ] [1x41 ] [1x22 ] [1x10 ] [1x7 ] [1x10 ] [1x16 ] [1x115 ] [1x39 ] [1x12 ] [1x34 ] [1x24 ] [1x7 ] [1x4 ] [1x17 ] [1x95 ] [1x16 ] [1x6 ] [1x6 ] [1x8 ] [1x32 ] [1x14 ] [1x46 ] [1x35 ] [1x29 ] [1x19 ] [1x5 ] [1x15 ] [1x15 ] [1x27 ] [1x5 ] [1x22 ] [1x14 ] [1x4 ] [1x4 ] [1x10 ] [1x4 ] [1x18 ] [1x21 ] [1x4 ] [1x4 ] [1x4 ] [1x4 ] [1x4 ] [1x2 ] [1x3 ] [1x6 ] [1x4 ] [1x3 ] [1x16 ] [1x2 ] [1x5 ] [1x6 ] [1x6 ] [1x7 ] [1x2 ] [1x2 ] [1x4 ] [1x8 ] [1x8 ] [1x4 ] [1x2 ] [1334]

  9. Experimental results Agglomerative Clustering Over subset(15 clusters) with good quality: [1x22 ] [1x20 ] [1x22 ] [1x20 ] [1x21 ] [1x36 ] [1x20 ] [1x21 ] [1x34 ] [1x23 ] [1x16 ] [1x12 ] [1x8 ] [1x4 ] [1x21 ]

  10. Experimental results MDS Visualization: (randomly chosen 7 classess)

  11. Experimental results MDS Visualization: (7 classes with good quality)

  12. Experimental results MDS Visualization: (random original configuration )

  13. Conclusion It’s feasible to transform image contour data to time sequence; Prior knowledge will improve the clustering results; The quality of data set decide the analysis result; MDS is a good method to visualize the clustering result.

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