Abstract
A novel algorithm of document clustering based on non-negative sparse analysis is proposed. In contrast to the algorithm based on non-negative matrix factorization, our algorithm can obtain documents topics exactly by controlling the sparseness of the topic matrix and the encoding matrix explicitly. Thus, the clustering accuracy has been improved greatly. In the end, simulation results are employed to further illustrate the accuracy and efficiency of this algorithm.
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Yang, C.F., Ye, M., Zhao, J. (2005). Document Clustering Based on Nonnegative Sparse Matrix Factorization. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_80
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DOI: https://doi.org/10.1007/11539117_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
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