SUBCLU

SUBCLU is an algorithm for clustering high-dimensional data by Karin Kailing, Hans-Peter Kriegel and Peer Kröger. It is a subspace clustering algorithm that builds on the density-based clustering algorithm DBSCAN. SUBCLU can find clusters in axis-parallel subspaces, and uses a bottom-up, greedy strategy to remain efficient.

SUBCLU uses a monotonicity criteria: if a cluster is found in a subspace ${\displaystyle S}$, then each subspace ${\displaystyle T\subseteq S}$ also contains a cluster. However, a cluster ${\displaystyle C\subseteq DB}$ in subspace ${\displaystyle S}$ is not necessarily a cluster in ${\displaystyle T\subseteq S}$, since clusters are required to be maximal, and more objects might be contained in the cluster in ${\displaystyle T}$ that contains ${\displaystyle C}$. However, a density-connected set in a subspace ${\displaystyle S}$ is also a density-connected set in ${\displaystyle T\subseteq S}$.

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