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2015 International Conference on Big Data and Smart Computing (BigComp) (2015)
Jeju, South Korea
Feb. 9, 2015 to Feb. 11, 2015
ISBN: 978-1-4799-7303-3
pp: 43-46
Won-Jo Lee , Department of Computer Science, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
Chae-Gyun Lim , Department of Computer Science, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
U Kang , Department of Computer Science, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
Ho-Jin Choi , Department of Computer Science, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
ABSTRACT
There are numerous 2-dimensional matrix data for clustering including a set of documents, citation networks, web graphs, etc. However, many real-world datasets have more than three modes which require at least 3-dimensional matrices or tensors. Focusing on the clustering algorithm known as cross-association, we extend the algorithm to deal with a 3-dimensional matrix. Our proposed method is fully automated, and simultaneously discovers clusters of both row, column, and tube groups. Experiments on real and synthetic datasets show that our method is effective. Through the proposed method, useful information can be obtained even from sparse datasets.
INDEX TERMS
Electron tubes, Tensile stress, Sparse matrices, Clustering algorithms, Indexes, Algorithm design and analysis, Complexity theory
CITATION

W. Lee, C. Lim, U. Kang and H. Choi, "An extension of the automatic cross-association method with a 3-dimensional matrix," 2015 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Jeju, South Korea, 2015, pp. 43-46.
doi:10.1109/35021BIGCOMP.2015.7072848
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