Advances in Knowledge Discovery and Data Mining: 16th by Hai-Long Nguyen, Yew-Kwong Woon, Wee-Keong Ng, Li Wan

By Hai-Long Nguyen, Yew-Kwong Woon, Wee-Keong Ng, Li Wan (auth.), Pang-Ning Tan, Sanjay Chawla, Chin Kuan Ho, James Bailey (eds.)

The two-volume set LNAI 7301 and 7302 constitutes the refereed court cases of the sixteenth Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2012, held in Kuala Lumpur, Malaysia, in may well 2012. the complete of 20 revised complete papers and sixty six revised brief papers have been rigorously reviewed and chosen from 241 submissions. The papers current new rules, unique study effects, and useful improvement stories from all KDD-related components. The papers are equipped in topical sections on supervised studying: energetic, ensemble, rare-class and on-line; unsupervised studying: clustering, probabilistic modeling within the first quantity and on trend mining: networks, graphs, time-series and outlier detection, and information manipulation: pre-processing and size aid within the moment volume.

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148–156 (1996) 9. : Stochastic gradient boosting. Computational Statistics & Data Analysis 38(4), 367–378 (2002) 10. : A theoretical and experimental analysis of linear combiners for multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6), 942–956 (2005) 11. : Diversity in Combinations of Heterogeneous Classifiers. -B. ) PAKDD 2009. LNCS, vol. 5476, pp. 923–932. Springer, Heidelberg (2009) 12. : Mining time-changing data streams. In: ACM SIGKDD, pp. 97–106.

The greedy expansion takes O(mh) time, where m is the number of edges, and h is the number of cliques. Fuzzy Clustering: Zhang [16] used the spectral method to embed the graph into low dimensionality Euclidean space. Nodes are then clustered by the fuzzy c-mean algorithm. Psorakis et al. [12] proposed a model based on Bayesian nonnegative matrix factorization (NMF). These algorithms need to determine the number of communities K and the use of matrix multiplication makes them inefficient. For NMF, the complexity is O(Kn2 ).

2. NMI as a function of the number Fig. 3. Omega as a function of the numof memberships Om in LFR ber of memberships Om in LFR We used networks with size n = 5000. The average degree is kept at k = 10. The degree of overlapping is determined by two parameters. On defines the number of overlapping nodes and is set to 10% of all nodes. Om defines the number of communities to which each overlapping node belongs and varies from 2 to 8 indicating the diversity of overlap. By increasing the value of Om , we create harder detection tasks.

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