Algorithm 1: Coordinate Clustering based on K-Means

Input: Cluster number, k; sample set, X = {X1, X2, …, XN};     Output: Cluster center, C = {C1, C2, …, Ck};

Randomly select k samples as the initial centroids {μ1, μ2, …, μk};     Repeat         $C_i=\varnothing ,(1\le i\le k)$         for j = 1, 2, …, N do           Calculate the distance between sample Xj and each mean vector ${\mathit{\mu }}_i(1\le i\le k)$: $d_{ij}=\parallel {\mathit{X}}_j-{\mathit{\mu }}_i{\parallel }_2$;           Determined the cluster market of Xj according to the nearest mean vector: ${\lambda }_i=\arg {\min }_{i\in \{1,2,\dots k\}}{d}_{ij}$;           Classify the sample Xj into the corresponding cluster: $C_{{\lambda }_j}=C_{{\lambda }_j}\cup \left\{{\mathit{X}}_j\right\}$;       end for       for i=1, 2, …, N do           Calculate new mean vector: ${\mathit{\mu }}_i^{\prime }=\frac{1}{\left|C_i\right|}{\sum }_{\mathit{X}\in C_i}\mathit{X}$;         if ${\mathit{\mu }}_i^{\prime }\ne {\mathit{\mu }}_i$ then             Update the current mean vector ${\mathit{\mu }}_i$ to ${\mathit{\mu }}_i^{\prime }$;         else             Keep the current mean vector unchanged;         end if       end for Until the current mean vector is not updated