A Scalable Parallel Subspace Clustering Algorithm for Massive Data Sets

Clustering is a data mining problem which finds dense regions in a sparse multi-dimensional data set. The attribute values and ranges of these regions characterize the clusters. Clustering algorithms need to scale with the data base size and also with the large dimensionality of the data set. Further, these algorithms need to explore the embedded clusters in a subspace of a high dimensional space. However, the time complexity of the algorithm to explore clusters in subspaces is exponential in the dimensionality of the data and is thus extremely compute intensive. Thus, parallelization is the choice for discovering clusters for large data sets. In this paper we present a scalable parallel subspace clustering algorithm which has both data and task parallelism embedded in it. We also formulate the technique of adaptive grids and present a truly un-supervised clustering algorithm requiring no user inputs. Our implementation shows near linear speedups with negligible communication overhea...

• Type: Conference
• Year: 2000
• Where: ICPP
• Authors: Harsha S. Nagesh, Sanjay Goil, Alok N. Choudhary
• Keywords: Clustering Algorithm, Data Sets, Distributed And Parallel Computing, ICPP 2000, Subspace Clustering Algorithm