Detecting Arbitrarily Oriented Subspace Clusters in Data Streams Using Hough Transform

ABSTRACT: When facing high-dimensional data streams, clustering algorithms quickly reach the boundaries of their usefulness as most of these methods are not designed to deal with the curse of dimensionality. Due to inherent sparsity in high-dimensional data, distances between objects tend to become meaningless since the distances between any two objects measured in the full dimensional space tend to become the same for all pairs of objects. In this work, we present a novel oriented subspace clustering algorithm that is able to deal with such issues and detects arbitrarily oriented subspace clusters in high-dimensional data streams. Data streams generally implicate the challenge that the data cannot be stored entirely and hence there is a general demand for suitable data handling strategies for clust

SUBMITTER: Borutta F 

PROVIDER: S-EPMC7206268 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature