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 court cases of the ninth foreign convention on complex info Mining and functions, ADMA 2013, held in Hangzhou, China, in December 2013.
The 32 usual papers and sixty four brief papers provided in those volumes have been rigorously reviewed and chosen from 222 submissions. The papers integrated in those volumes conceal the subsequent subject matters: opinion mining, habit mining, facts circulate mining, sequential info mining, internet mining, snapshot mining, textual content mining, social community mining, type, clustering, organization rule mining, trend mining, regression, predication, characteristic extraction, identity, privateness maintenance, functions, and computing device learning.
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Extra resources for Advanced Data Mining and Applications: 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part II
18, pp. 89–96 (2003) 22. : Clustering ensembles using genetic algorithm. In: Proceedings of the International Workshop on Computer Architecture for Machine Perception and Sensing, pp. 119–123. IEEE (2007) 23. : Integration analysis of diverse genomic data using multi-clustering results. , Brause, R. ) ISBMDA 2006. LNCS (LNBI), vol. 4345, pp. 37–48. Springer, Heidelberg (2006) 24. : Recursive self-organizing maps with hybrid clustering. In: Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems, pp.
Step2. If we cannot find a r ∈ R or we find a r ∈ R in step1 has || r − x ||2 larger than a predefined value γ , then we adds a new representative data point rnew into R , and encode x with rnew . Otherwise, we use r ∈ R chosen in step 1 to encode x . Thus our method will add a new representative data point rnew into R (the codebook) when we can’t find a r ∈ R in step 1 or r ∈ R chosen in step 1 has || r − x ||2 > γ . It should be noted that θ and γ are closely related to representative data set distortion, thus gives a better interpretation for the final clustering results than predefined representative data point number in .
Wang et al. 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.
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.)