Data decomposition techniques for parallel tree-based k-means clustering
buir.advisor | Gürsoy, Atilla | |
dc.contributor.author | Şen, Cenk | |
dc.date.accessioned | 2016-01-08T18:25:35Z | |
dc.date.available | 2016-01-08T18:25:35Z | |
dc.date.issued | 2002 | |
dc.department | Department of Computer Engineering | en_US |
dc.description | Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2002. | en_US |
dc.description | Thesis (Master's) -- Bilkent University, 2002. | en_US |
dc.description | Includes bibliographical references leaves 78-80. | en_US |
dc.description.abstract | The main computation in the k-means clustering is distance calculations between cluster centroids and patterns. As the number of the patterns and the number of centroids increases, time needed to complete computations increased. This computational load requires high performance computers and/or algorithmic improvements. The parallel tree-based k-means algorithm on distributed memory machines combines the algorithmic improvements and high computation capacity of the parallel computers to deal with huge datasets. Its performance is affected by the data decomposition technique used. In this thesis, we presented novel data decomposition technique to improve the performance of the parallel tree-based k-means algorithm on distributed memory machines. Proposed tree-based decomposition techniques try to decrease the total number of the distance calculations by assigning processors compact subspaces. The compact subspace improves the performance of the pruning function of the tree-based k-means algorithm. We have implemented the algorithm and have conducted experiments on a PC cluster. Our experimental results demonstrated that the tree-based decomposition technique outperforms the random decomposition and stripwise decomposition techniques. | en_US |
dc.description.degree | M.S. | en_US |
dc.description.provenance | Made available in DSpace on 2016-01-08T18:25:35Z (GMT). No. of bitstreams: 1 0002066.pdf: 1831069 bytes, checksum: 1ba08a7499eced7f1afce4d86930e4e1 (MD5) | en |
dc.description.statementofresponsibility | Şen, Cenk | en_US |
dc.format.extent | xii, 80 leaves, illustraions, tables | en_US |
dc.identifier.itemid | BILKUTUPB066942 | |
dc.identifier.uri | http://hdl.handle.net/11693/15853 | |
dc.language.iso | English | en_US |
dc.publisher | Bilkent University | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Clustering | en_US |
dc.subject | Parallel algorithm | en_US |
dc.subject | Load balancing | en_US |
dc.subject | Data decomposition | en_US |
dc.subject.lcc | QA76.58 .S46 2002 | en_US |
dc.subject.lcsh | Data mining. | en_US |
dc.subject.lcsh | Computer algorithms. | en_US |
dc.title | Data decomposition techniques for parallel tree-based k-means clustering | en_US |
dc.type | Thesis | en_US |
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