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 rigorously reviewed and chosen from 222 submissions. The papers incorporated in those volumes disguise the next themes: opinion mining, habit mining, info circulate mining, sequential info mining, internet mining, picture mining, textual content mining, social community mining, category, clustering, organization rule mining, trend mining, regression, predication, function extraction, id, privateness upkeep, functions, and computer 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
When γ is set to be larger than the maximal distance of the data point pairs, then for each uncoded data point in X , growing vector quantization algorithm will add a new representative data point to R if R is empty or the distortions of representative data points in r ∈ R all exceeds θ . It means that new representative data point will be added when all r ∈ R has D(r ) > θ . Given a r ∈ R and maximal distortion θ , the minimal number of data points in X that can be encoded by r is θ / γ , thus the n data points in X can be encoded by at most nγ / θ representative data points of R .
It has been pointed in  that all of these three solutions neglect the important connection between the decreasing of the original data set by a preprocessing method and the subsequent effect on the clustering. In , Yan et. al. proposed two fast approximate spectral clustering algorithms based on rate-distortion theory. The first method in  is a local k-means clustering algorithm (KASP) (for the information theoretical aspect of k-means algorithm, please refer to ). It needs a user specific parameter k (representative point number in k-means for KASP), which is hard for common user to specify.
34–46, 2013. c Springer-Verlag Berlin Heidelberg 2013 A Novel Deterministic Sampling Technique 35 algorithm  employs random sampling in clustering and the speedups obtained are very good. In this paper we propose a novel deterministic sampling technique that can be used to speedup any clustering algorithm. To the best of our knowledge, deterministic sampling has not been utilized before in clustering. Let I be a given set of n objects. It helps to assume that the objects are points in d for some large d.