  Subspace clustering aims to find groups of similar objects (clusters) that
exist in lower dimensional subspaces from a high dimensional dataset. It has a
wide range of applications, such as analysing high dimensional sensor data or
DNA sequences. However, existing algorithms have limitations in finding
clusters in non-disjoint subspaces and scaling to large data, which impinge
their applicability in areas such as bioinformatics and the Internet of Things.
We aim to address such limitations by proposing a subspace clustering algorithm
using a bottom-up strategy. Our algorithm first searches for base clusters in
low dimensional subspaces. It then forms clusters in higher-dimensional
subspaces using these base clusters, which we formulate as a frequent pattern
mining problem. This formulation enables efficient search for clusters in
higher-dimensional subspaces, which is done using FP-trees. The proposed
algorithm is evaluated against traditional bottom-up clustering algorithms and
state-of-the-art subspace clustering algorithms. The experimental results show
that the proposed algorithm produces clusters with high accuracy, and scales
well to large volumes of data. We also demonstrate the algorithm's performance
using real-life data, including ten genomic datasets and a car parking
occupancy dataset.
