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Subspace clustering aims to find groups of similar objects (clusters) thatexist in lower dimensional subspaces from a high dimensional dataset. It has awide range of applications, such as analysing high dimensional sensor data orDNA sequences. However, existing algorithms have limitations in findingclusters in non-disjoint subspaces and scaling to large data, which impingetheir applicability in areas such as bioinformatics and the Internet of Things.We aim to address such limitations by proposing a subspace clustering algorithmusing a bottom-up strategy. Our algorithm first searches for base clusters inlow dimensional subspaces. It then forms clusters in higher-dimensionalsubspaces using these base clusters, which we formulate as a frequent patternmining problem. This formulation enables efficient search for clusters inhigher-dimensional subspaces, which is done using FP-trees. The proposedalgorithm is evaluated against traditional bottom-up clustering algorithms andstate-of-the-art subspace clustering algorithms. The experimental results showthat the proposed algorithm produces clusters with high accuracy, and scaleswell to large volumes of data. We also demonstrate the algorithm's performanceusing real-life data, including ten genomic datasets and a car parkingoccupancy dataset.

Scalable Bottom-up Subspace Clustering using FP-Trees for High Dimensional Data