DCLUSTM Dissimilarity based multilevel clustering by kNN modeseeking.
LAB = DCLUSTM(D,K)
DescriptionA kNN modeseeking method is used to assign each object to its nearest density mode. Object densities are related to the distances to neighbors. Modes are determined by recusively jumping to objects in the neighborhood with the highest density. As many clusters are found as there are objects that are the mode in their own neighborhood. Modeseeking clustering does not return a predefined number of clusters. Determining a clustering with exactly K clusters is done by RECLUSTK. It might reduce the perfomance. This routine is based on the same algorithm as CLUSTM (for feature based data), which uses MODECLUST and MODECLUSTF. For reasons of speed it is implemented in another way, so results may differ slightly. Clusterings can be evaluated by CLUSTEVAL, CLUSTCERR or CLUSTC on the basis of (some) true labels. Reference(s)Cheng, Y. "Mean shift, mode Seeking, and clustering", IEEE Transactions on PAMI, vol. 17, no. 8, pp. 790799, 1995. R.P.W. Duin, A.L.N. Fred, M. Loog, and E. Pekalska, Mode Seeking Clustering by KNN and Mean Shift Evaluated, Proc. SSPR & SPR 2012, LNCS, vol. 7626, Springer, 2012, 5159. See alsodatasets, mappings, dclustk, dclusth, dcluste, modeclust, modeclustf, kclust, reclustn, clusteval, clustcerr, clustc,
