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A novel ant-based clustering algorithm using the kernel method

A novel ant-based clustering algorithm using the kernel method. Lei Zhang*, Qixin Cao 2011, InfSci Presented by Chien-Hao Kung 2011/10/6. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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A novel ant-based clustering algorithm using the kernel method

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  1. A novel ant-based clustering algorithm using the kernel method Lei Zhang*, Qixin Cao 2011, InfSci Presented by Chien-Hao Kung 2011/10/6

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • Ant-based clustering is not highly efficient because of the randomness in the algorithm. • Ant-based clustering algorithms are distance-based, if the separation boundaries between clusters are nonlinear, however, the algorithms will fail.

  4. Objectives • There are two aspects to the integration. • Kernel principal component analysis(KPCA) is applied to modify the random projection of objects. • To use kernel function calculated the distance between the objects in input space as a similarity measure.

  5. Methodology Grid Object Ant The ant-based clustering algorithm

  6. Methodology • Kernel-based clustering • Mercer kernels • Commonly used kernel functions • Kernel-based clustering

  7. Methodology Enlarging Rounding Shifting • The novel ant-based clustering with the kernel method(ACK)

  8. Methodology • Parameter setting • Size of the projection plane • Radius r • Kernel size • Scaling parameter • Threshold

  9. Experiments • Evaluation functions • The F-measure (F) • The Dunn Index (DI) • The inner cluster variance (ICV) • The error rate (ER) • Time cost (T)

  10. Experiments Experimental data

  11. Experiments

  12. Experiments

  13. Experiments

  14. Experiments

  15. Experiments

  16. Experiments

  17. Experiments

  18. Conclusions • The algorithm has some new characteristics • The algorithm can deal with some datasets with non-Gaussian distribution because of the incorporation of the kernel function. • The projection based on KPCA creates rough clusters, which reduces the running time and increases the algorithm’s efficiency. • Performing clustering in the feature space after kernel mapping can improve clustering quality.

  19. Comments • Advantages • The paper has some wrong. Application • Ant-based clustering

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