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Fuzzy Clustering with Principal C omponent Analysis

Fuzzy Clustering with Principal C omponent Analysis. Similarity Fuzzy Cluster(SFC). Layer 1. 計算單一維度與各群聚間的單維高斯歸屬度 每一 節點表示為一群 每 一群裡的子節點為維度 (n) 個數. Layer 2. Layer 3. 競爭 式運算節點. Layer 4. Hard limit function. SFC 範例. Cluster Merge. 減少多餘的 cluster. Merge 範例. 為何需要 PCA.

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Fuzzy Clustering with Principal C omponent Analysis

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  1. Fuzzy Clustering with Principal Component Analysis

  2. Similarity Fuzzy Cluster(SFC)

  3. Layer 1 • 計算單一維度與各群聚間的單維高斯歸屬度 • 每一節點表示為一群 • 每一群裡的子節點為維度(n)個數

  4. Layer 2

  5. Layer 3 • 競爭式運算節點

  6. Layer 4 • Hard limit function

  7. SFC範例

  8. Cluster Merge • 減少多餘的cluster

  9. Merge 範例

  10. 為何需要PCA

  11. Principal Component Analysis • 將 input space 的資料經由transformation matrix W 投影到另一個空間系統 • 資料群中變化量最大的方向即為第一主成份方向，其次為第二主成份方向，以此類推 • 各個主成份方向之間為正交

  12. PC-SFC

  13. Layer 1

  14. Layer 5

  15. PC-SFC範例

  16. Online cluster merge • 降低時間 • |b|=0 新增cluster • |b|=1 找出和資料點最相似的cluster

  17. |b| • 表示有兩個以上的cluster和資料點相似 • 將和其他可能相似的 cluster

  18. Re-assign

  19. 實驗架構 1.SFC-PC-RE-M 2.PC-SFC-RE-M 3.PC-OM-SFC_RE

  20. 實驗一

  21. 1.當cluster資料點越多表示該cluster中心越為穩定1.當cluster資料點越多表示該cluster中心越為穩定 2.初始標準差越大，分群越少

  22. 實驗二

  23. 實驗三

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