输入:样本集 D = { x 1 , x 2 , x 3 , . . . , x m } D=\left \{ x_{1},x_{2},x_{3},...,x_{m} \right \} D={x1,x2,x3,...,xm};聚类簇数k. 过程: 1:从D中随机选择k个样本作为初始均值向量 { μ 1 , μ 2 , μ 3 , . . . , μ k } \left \{ \mu _{1},\mu _{2},\mu _{3},...,\mu _{k} \right \} {μ1,μ2,μ3,...,μk} 2:repeat 3: 令 C i = ∅ ( 1 ⩽ i ⩽ k ) C_{i}=\varnothing (1\leqslant i\leqslant k) Ci=∅(1⩽i⩽k) 4: for j=1,2,…,m do 5: 计算样本 x j x_{j} xj与各均值向量 μ i ( 1 ⩽ i ⩽ k ) \mu_{i}(1\leqslant i\leqslant k) μi(1⩽i⩽k)的距离: d j i = ∥ x j − μ i ∥ 2 d_{ji}=\left \| x_{j}-\mu_{i} \right \|_{2} dji=∥xj−μi∥2; 6: 根据距离最近的均值向量确定 x j x_{j} xj的簇标记: λ j = a r g m i n i ∈ { 1 , 2 , 3 , . . . , k } d j i \lambda _{j}=arg min_{i\in \left \{ 1,2,3,...,k \right \}}d_{ji} λj=argmini∈{1,2,3,...,k}dji; 7: 将样本 x j x_{j} xj划入相应的簇: C λ j = C λ j ∪ { x j } ; C_{\lambda_{j}}=C_{\lambda_{j}}\cup \left \{ x_{j} \right \}; Cλj=Cλj∪{xj}; 8: end for 9: for i=1,2,…,k do 10: 计算新均值向量: μ i ′ = 1 ∣ C i ∣ ∑ x ∈ C i x \mu_{i}^{'}=\frac{1}{\left | C_{i} \right |}\sum _{x\in C_{i}}x μi′=∣Ci∣1∑x∈Cix; 11: if μ i ′ ≠ μ i \mu_{i}^{'}\neq \mu_{i} μi′=μi then 12: 将当前均值向量 μ i \mu_{i} μi更新为 μ i ′ \mu_{i}^{'} μi′ 13: else 14: 保持当前均值不变 15: end if 16: end for 17:until 当前均值向量均未更新 输出:簇划分 C = { C 1 , C 2 , . . . , C k } C=\left \{ C_{1} ,C_{2},...,C_{k} \right \} C={C1,C2,...,Ck} |
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来自: AetherCore > 《计算机》