By Yong-Bin Kang, Shonali Krishnaswamy (auth.), Jie Tang, Irwin King, Ling Chen, Jianyong Wang (eds.)
The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed court cases of the seventh foreign 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 awarded including three keynote speeches have been rigorously reviewed and chosen from 191 submissions. The papers conceal a variety of issues offering unique study findings in info mining, spanning purposes, algorithms, software program and structures, and utilized disciplines.
Read Online 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
Sample text
If ws(x, y) > 0 and a strong dissimilarity is detected we do not state that x and y are overall similar or not, and s(x, y) = 0. If ws(x, y) < 0 and, a strong dissimilarity is observed, then x and y are certainly not overall similar and s(x, y) = −1. Finally, if ws(x, y) = 0 is observed conjointly with a strong dissimilarity, we will conclude that x and y are indeed not overall similar and s(x, y) is put to −1. Property: The overall dissimilarity represents the negation of the overall similarity: d = −s.
Finally, experiment results are provided for validating our proposal over some known data sets. Keywords: Distributed clustering, Graph, Privacy-preserving data. 1 Introduction Data mining is the process of extracting patterns from data and transforming it into knowledge. It deals with a large field of research with some known problems which are related to data: their huge quantity, high dimensionality and complex structure.
Edges valued by −1 are not represented and the zero-valued one is dashed. As a Condorcet similarity graph is always reflexive, we do not represent the loops on the nodes. 00 a c d b Fig. 2. Encoding of the attributes (left) and bipolar-valued similarity relation with associated Condorcet similarity graph (right) 3 Definition of the Clusters Ideally, a cluster would have all the objects inside it similar to each other and dissimilar from the rest. In graph theory this may be modeled by a maximal clique, however, we would also need the maximal clique to be totally disconnected from the rest of the graph, which on real data will very rarely be the case.