The Expansion of Initial Point Algorithm for K-Modes Algorithm

The determination of the starting point in the k-modes algorithm is taken by random. Of course, such a thing can lead to an iteration of unpredictable numbers and accuracy. Therefore, it is necessary to develop different algorithms that are used to determine the starting point with hierarchical agglomerative clustering approach instead of randomly selecting the starting point in the initial iteration. At the end of this research is expected clustering process can produce more efficient iteration. The result of determining the value generated in this algorithm is the incorporation of a number of cluster central points on the variables based on the calculation of the approach which has the average linkage algorithm. Followed by calculating the difference of objective function on each iteration, of course, finished clustering process on k-modes. Iteration will stop after the difference of objective function is smaller than the specified limit.