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The low value of minPts = 1 does not make sense, as then every point is a core point by definition. With minPts ≤ 2, the result will be the same as of hierarchical clustering with the single link metric, with the dendrogram cut at height ε. Therefore, minPts must be chosen at least 3. However, larger values are usually better for data sets ...
Like DBSCAN, OPTICS requires two parameters: ε, which describes the maximum distance (radius) to consider, and MinPts, describing the number of points required to form a cluster. A point p is a core point if at least MinPts points are found within its ε -neighborhood N ε ( p ) {\displaystyle N_{\varepsilon }(p)} (including point p itself).
SUBCLU is an algorithm for clustering high-dimensional data by Karin Kailing, Hans-Peter Kriegel and Peer Kröger. [1] It is a subspace clustering algorithm that builds on the density-based clustering algorithm DBSCAN.
DBSCAN(D, eps, MinPts) C = 0 for each unvisited point P in dataset D mark P as visited N = getNeighbors (P, eps) if sizeof(N) < MinPts mark P as NOISE else C = next ...
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The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]
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