By Amjad Mahmood, Tianrui Li, Yan Yang, Hongjun Wang (auth.), Hiroshi Motoda, Zhaohui Wu, Longbing Cao, Osmar Zaiane, Min Yao, Wei Wang (eds.)
The two-volume set LNAI 8346 and 8347 constitutes the completely refereed lawsuits of the ninth overseas convention on complex information Mining and purposes, ADMA 2013, held in Hangzhou, China, in December 2013.
The 32 usual papers and sixty four brief papers provided in those volumes have been conscientiously reviewed and chosen from 222 submissions. The papers incorporated in those volumes conceal the next issues: opinion mining, habit mining, facts flow mining, sequential facts mining, net mining, picture mining, textual content mining, social community mining, type, clustering, organization rule mining, trend mining, regression, predication, function extraction, id, privateness maintenance, functions, and laptop learning.
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Additional resources for Advanced Data Mining and Applications: 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part II
Conclusion A fast spectral clustering algorithm for large data set is proposed in this paper. Based on the minimization of the increment of distortion, we develop a novel efficient growing vector quantization method to preprocess a large scale data set, which can compresses the original data set into a small set of representative data points in one scan of the original data set. Then we apply spectral clustering algorithm to the small set. Experiments on real data sets show that our method provides fast and accurate clustering results.
F1 -measure takes value from [0, 1], and a larger value implies higher quality of clustering; while the recovery level of k is the difference ratio of the selected k to the ground truth, where smaller values indicate better recovery of the number of clusters. 20 Y. Wang et al. 1 Numerical Comparison To investigate the numerical performance of the proposed optimization methods, we randomly generate a problem with 20 vertices and 2 clusters. We first find the exact solution to problem (1) by searching over all possible clustering assignments, which adds i up to 20 i=1 C20 = 1, 048, 575 clusterings, and choosing the one with the minimum objective value.
Co-clustering of image segments using convex optimization applied to em neuronal reconstruction. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2203–2210 (2010) 11. : Interior-Point Polynomial Algorithms in Convex Programming. Society for Industrial and Applied Mathematics (1994) 12. : Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001) 13. : On the Statistical Analysis of Dirty Pictures.
Advanced Data Mining and Applications: 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part II by Amjad Mahmood, Tianrui Li, Yan Yang, Hongjun Wang (auth.), Hiroshi Motoda, Zhaohui Wu, Longbing Cao, Osmar Zaiane, Min Yao, Wei Wang (eds.)