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 complicated information Mining and functions, ADMA 2013, held in Hangzhou, China, in December 2013.
The 32 average 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 hide the subsequent themes: opinion mining, habit mining, information circulation mining, sequential info mining, internet mining, photograph mining, textual content mining, social community mining, category, clustering, organization rule mining, trend mining, regression, predication, function extraction, identity, privateness protection, purposes, and desktop learning.
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Extra info for Advanced Data Mining and Applications: 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part II
2 Evaluation Metrics We test the three methods: GVQ+Spectral clustering, KASP, spectral clustering by computing Clustering Accuracy (CA) and Normalized Mutual Information (NMI) on the labels generated by the three methods and their real labels. Clustering accuracy and Normalized information have been defined and used in . It should be noted that for the spectral clustering algorithm used in these three method, we choose to construct 20-nearest neighbor graph and gaussian similarity function with variance calculated in a self-tuning way suggested by .
In this section we will introduce how to initialize L. Apart from that, we will also look into the sparsity issue in data set. At last, we will analyse the computational complexity. In principle, any L which satisfies (2) is legal. An example is to initialize L with 2I n×n − 1. e. the initial number of clusters) to a relatively large value. On the other hand, as the number of rows of initial L grows, the time complexity of the algorithm increases and the speed of convergence decreases. Here we describe a heuristic initialization of L based on the positive degree of vertices.
Discov. Data 1(1) (March 2007) 15. 02, a Matlab toolbox for optimization over symmetric cones. Optimization Methods and Software 11-12 (1999) 16. : Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(5), 898–916 (2011) 17. : Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000) 18. : The link prediction problem for social networks. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, CIKM 2003, pp.
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.)