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Title: | A Parallel Framework for Grid-Based Bottom-Up Subspace Clustering |
Authors: | Goyal, Navneet Goyal, Poonam |
Keywords: | Computer Science Subspace clustering Bottom-up Clique Parallel framework |
Issue Date: | 2016 |
Publisher: | IEEE |
Abstract: | Clustering is a popular data mining and machine learning technique which discovers interesting patterns from unlabeled data by grouping similar objects together. Clustering high-dimensional data is a challenging task as points in high dimensional space are nearly equidistant from each other, rendering commonly used similarity measures ineffective. Subspace clustering has emerged as a possible solution to the problem of clustering high-dimensional data. In subspace clustering, we try to find clusters in different subspaces within a dataset. Many subspace clustering algorithms have been proposed in the last two decades to find clusters in multiple overlapping subspaces of high-dimensional data. Subspace clustering algorithms iteratively find the best subset of dimensions for a cluster from 2d-1 possible combinations in d-dimensional data. Subspace clustering is extremely compute intensive because of exhaustive search of subspaces, especially in the bottom-up subspace clustering algorithms. To address this issue, an efficient parallel framework for grid-based bottom-up subspace clustering algorithms is developed, considering popular algorithms belonging to this category. The framework is implemented for shared memory, distributed memory, and hybrid systems and is tested for three grid-based bottom-up subspace clustering algorithms: CLIQUE, MAFIA, and ENCLUS. All parallel implementations exhibit impressive speedup and scalability on real datasets. |
URI: | https://ieeexplore.ieee.org/document/7796919 http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8124 |
Appears in Collections: | Department of Computer Science and Information Systems |
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