Algorithm 3: Pseudocode for high-dimensional clustering based on k-adjacent cells searching.

Input:$\mathrm{dataset} D^d$, multidimensional grid space $G^d$

Output: cluster label, cluster density, cluster number m

1: Initialization; 2: Rank all cells in descending order of density; 3: $M\leftarrow$ $\mathrm{cells} \mathrm{number} \mathrm{of} G^d$; 4: Traverse all grids; for the ith cell, construct its k-adjacent vector from the (i + 1)th to Mth     cells according to Equation (16); 5: $m\leftarrow$ 0 6: For i = 1 to M Do 7:    If the ith cell exists, Then 8:      $m\leftarrow$ m + 1; 9:      Label the ith cell to be the mth cluster; 10:    Find the k-adjacent cells of the ith cell; 11:    Label these k-adjacent cells to be the mth cluster; 12:    Delete these k-adjacent cells and the current cell; 13: end If 14: end For 15: Label the data points according to their cells; 16: Calculate the mean density of the cells of each cluster;