Introduction: PROCLUS (Projected Clustering) is a typical dimension-reduction subspace clustering method. That is, instead of starting from single-dimensional spaces, it starts by finding an initial approximation of the clusters in the high-dimensional attribute space. Each dimension is then assigned a weight for each cluster, and the updated weights are used in the next iteration to regenerate the clusters. This leads to the exploration of dense regions in all subspaces of some desired dimensionality and avoids the generation of a large number of overlapped clusters in projected dimensions of lower dimensionality.
PROCLUS finds the best set of medoids by a hill-climbing process similar to that used in CLARANS, but generalized to deal with projected clustering. It adopts a distance measure called Manhattan segmental distance, which is the Manhattan distance on a set of relevant dimensions. The PROCLUS algorithm consists of three phases: initialization, iteration, and cluster refinement. In the initialization phase, it uses a greedy algorithm to select a set of initial medoids that are far apart from each other so as to ensure that each cluster is represented by at least one object in the selected set. More concretely, it first chooses a random sample of data points proportional to the number of clusters we wish to generate, and then applies the greedy algorithm to obtain an even smaller final subset for the next phase. The iteration phase selects a random set of k medoids from this reduced set (of medoids), and replaces “bad” medoids with randomly chosen new medoids if the clustering is improved. For each medoid, a set of dimensions is chosen whose average distances are small compared to statistical expectation. The total number of dimensions associated to medoids must be kl, where l is an input parameter that selects the average dimensionality of cluster subspaces. The refinement phase computes new dimensions for each medoid based on the clusters found, reassigns points to medoids, and removes outliers.
Experiments on PROCLUS show that the method is efficient and scalable at finding high-dimensional clusters. Unlike CLIQUE, which outputs many overlapped clusters, PROCLUS finds non overlapped partitions of points. The discovered clusters may help better understand the high-dimensional data and facilitate other subsequence analyses.
