Scalable subspace clustering for high-dimensional data.
problem Finding clusters in non-disjoint subspaces and scaling to large data.
method Bottom-up strategy using FP-trees for frequent pattern mining.
result The proposed algorithm produces clusters with high accuracy and scales well to large data.