Download Advanced Data Mining and Applications: 7th International by Yong-Bin Kang, Shonali Krishnaswamy (auth.), Jie Tang, Irwin PDF

By Yong-Bin Kang, Shonali Krishnaswamy (auth.), Jie Tang, Irwin King, Ling Chen, Jianyong Wang (eds.)

ISBN-10: 3642258522

ISBN-13: 9783642258527

The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed complaints of the seventh overseas convention on complicated facts Mining and functions, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised complete papers and 29 brief papers offered including three keynote speeches have been rigorously reviewed and chosen from 191 submissions. The papers conceal quite a lot of themes proposing unique study findings in facts mining, spanning purposes, algorithms, software program and platforms, and utilized disciplines.

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Read or Download Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part I PDF

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Extra resources for Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part I

Example text

2 Dual Similarity-Dissimilarity Modelling To illustrate the relational concepts of similarity and dissimilarity we first present a small didactic problem. Let us consider in Figure 1 a set of objects {a, b, c, d} that are described by four attributes, one cardinal and three ordinal. We may notice that objects a, b and c are quite small, while d is significantly larger. On the second attribute a and b, as well as c and d have the same texture. On the color attribute we notice some objects are dark, and some are light or we could consider each color level to be different.

IEEE Transactions on Knowledge and Data Engineering 18(2), 202–216 (2006) 27. : A Regression-Based Temporal Pattern Mining Scheme for Data Streams. In: Proc. VLDB (2003) 28. : Weighted Association Rule Mining using Weighted Support and Significance Framework. In: Proc. SIGKDD (2003) 29. : estMax: Tracing Maximal Frequent Itemsets over Online Data Streams. In: Proc. ICDM (2007) 30. : Online Mining(Recently) Maximal Frequent Itemsets over Data Streams. In: Proc. RIDE (2005) 31. : Efficiently Mining Maximal Frequent Itemsets.

Several researchers have illustrated that data privacy is an important and inevitable constraint when dealing with distributed knowledge discovery. The challenge is to obtain valid results while preserving this property in each related party. In this paper, we propose a new approach based on enrichment of graphs where each party does the cluster of each entity (instance), but does nothing about the attributes (features or variables) of the other parties. Furthermore, no information is given about the clustering algorithms which provide the different partitions.

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