Industrial

New PDF release: Advances in Data Mining. Applications and Theoretical

By Giorgio Giacinto (auth.), Petra Perner (eds.)

ISBN-10: 3642144004

ISBN-13: 9783642144004

These are the complaints of the 10th occasion of the commercial convention on facts Mining ICDM held in Berlin (www.data-mining-forum.de). For this variation this system Committee acquired one hundred seventy five submissions. After the pe- evaluate technique, we accredited forty nine fine quality papers for oral presentation which are integrated during this ebook. the themes variety from theoretical facets of information mining to app- cations of information mining corresponding to on multimedia information, in advertising, finance and telec- munication, in drugs and agriculture, and in technique keep an eye on, and society. prolonged types of chosen papers will look within the foreign magazine Trans- tions on laptop studying and information Mining (www.ibai-publishing.org/journal/mldm). Ten papers have been chosen for poster shows and are released within the ICDM Poster continuing quantity via ibai-publishing (www.ibai-publishing.org). together with ICDM 4 workshops have been hung on exact scorching applicati- orientated issues in info mining: info Mining in advertising and marketing DMM, info Mining in LifeScience DMLS, the Workshop on Case-Based Reasoning for Multimedia info CBR-MD, and the Workshop on facts Mining in Agriculture DMA. The Workshop on facts Mining in Agriculture ran for the 1st time this 12 months. All workshop papers should be released within the workshop court cases through ibai-publishing (www.ibai-publishing.org). chosen papers of CBR-MD may be released in a distinct factor of the foreign magazine Transactions on Case-Based Reasoning (www.ibai-publishing.org/journal/cbr).

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Fr Abstract. Many real world systems can be modeled as networks or graphs. Clustering algorithms that help us to organize and understand these networks are usually referred to as, graph based clustering algorithms. Many algorithms exist in the literature for clustering network data. Evaluating the quality of these clustering algorithms is an important task addressed by different researchers. An important ingredient of evaluating these clustering techniques is the node-edge density of a cluster. In this paper, we argue that evaluation methods based on density are heavily biased to networks having dense components, such as social networks, but are not well suited for data sets with other network topologies where the nodes are not densely connected.

1 Sequence Searching Researchers using genetic data frequently are interested in finding similar sequences. Given a particular sequence, for example newly discovered, they search online databases for similar known sequences, such as previously sequenced DNA segments, or genes, not only from humans, but also from varied organisms. For example, in drug design, they would like to know which protein would be encoded by a new sequence by matching it with similar sequences coding for proteins in the protein database SWISSPROT.

In linear systems, correlation can be measured by linear correlation coefficient. r= i (xi − xi )(yi − yi ) 2 2 (x i i − xi ) i (yi − yi ) (1) However, most systems in real world applications are non-linear. Correlation in non-linear systems can be measured by using Symmetrical Uncertainty (SU). SU = 2 IG(X|Y ) H(X)H(Y ) (2) IG(X, Y ) = H(X) − H(X|Y ) (3) H(X) = − (4) P (xi )log2 P (xi ) i where IG(X|Y ) is the Information Gain of X after observing variable Y . H(X) and H(Y ) are the entropy of variable X and Y , respectively.

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Advances in Data Mining. Applications and Theoretical Aspects: 10th Industrial Conference, ICDM 2010, Berlin, Germany, July 12-14, 2010. Proceedings by Giorgio Giacinto (auth.), Petra Perner (eds.)


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