By Dawn E. Holmes, Lakhmi C Jain
There are many precious books to be had on facts mining conception and functions. even if, in compiling a quantity titled “DATA MINING: Foundations and clever Paradigms: quantity 1: Clustering, organization and type” we want to introduce a few of the most up-to-date advancements to a extensive viewers of either experts and non-specialists during this field.
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Additional info for Data Mining: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification
Introduction to Algorithms, 2nd edn. MIT Press and McGraw-Hill (2001) Clustering Analysis in Large Graphs with Rich Attributes 27 30. : SimRank: a measure of structural-context similarity. In: Proc. 2002 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2002), Edmonton, Canada, pp. 538–543 (July 2002) 31. : Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications 10(2), 191–218 (2006) 32. : Fast random walk with restart and its applications.
In: Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Paris, France, June 28-July 1, pp. 857–866 (2009) 10. : Spatio-temporal patterns in network events. In: Proceedings of the 6th ACM International Conference on Emerging Networking Experiments and Technologies (CoNEXT 2010), NY, USA, November 30-December 3 (2010) 26 Y. Zhou and L. Liu 11. : A distributed approach to node clustering in decentralized peer-to-peer networks. IEEE Transactions on Parallel and Distributed Systems (TPDS) 16, 1–16 (2005) 12.
In this step, a multi-dimensional access method such as an R-tree family can be used for a fast sequence search [21,18,26,14]. However, the computational costs of ﬁrst generating the support time sequences of all combinatorial candidate itemsets and then doing the similarity search become prohibitively expensive with increase of items. Thus it is crucial to devise schemes to reduce the itemset search space eﬀectively for eﬃcient computation. We ﬁrst present the design concept of similarity-proﬁled temporal association mining algorithm.
Data Mining: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification by Dawn E. Holmes, Lakhmi C Jain