141 research outputs found
A Distributed Block Chebyshev-Davidson Algorithm for Parallel Spectral Clustering
We develop a distributed Block Chebyshev-Davidson algorithm to solve
large-scale leading eigenvalue problems for spectral analysis in spectral
clustering. First, the efficiency of the Chebyshev-Davidson algorithm relies on
the prior knowledge of the eigenvalue spectrum, which could be expensive to
estimate. This issue can be lessened by the analytic spectrum estimation of the
Laplacian or normalized Laplacian matrices in spectral clustering, making the
proposed algorithm very efficient for spectral clustering. Second, to make the
proposed algorithm capable of analyzing big data, a distributed and parallel
version has been developed with attractive scalability. The speedup by parallel
computing is approximately equivalent to , where denotes the
number of processes. {Numerical results will be provided to demonstrate its
efficiency in spectral clustering and scalability advantage over existing
eigensolvers used for spectral clustering in parallel computing environments.
K-tree: Large Scale Document Clustering
We introduce K-tree in an information retrieval context. It is an efficient
approximation of the k-means clustering algorithm. Unlike k-means it forms a
hierarchy of clusters. It has been extended to address issues with sparse
representations. We compare performance and quality to CLUTO using document
collections. The K-tree has a low time complexity that is suitable for large
document collections. This tree structure allows for efficient disk based
implementations where space requirements exceed that of main memory.Comment: 2 pages, SIGIR 200
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