15. PAKDD 2011: Shenzhen, China

Joshua Zhexue Huang, Longbing Cao, Jaideep Srivastava (Eds.): Advances in Knowledge Discovery and Data Mining - 15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings, Part II. Lecture Notes in Computer Science 6635 Springer 2011, ISBN 978-3-642-20846-1

Graph Mining

Leting Wu, Xiaowei Ying, Xintao Wu, Aidong Lu, Zhi-Hua Zhou:
Spectral Analysis of k-Balanced Signed Graphs. 1-12
U. Kang, Brendan Meeder, Christos Faloutsos:
Spectral Analysis for Billion-Scale Graphs: Discoveries and Implementation. 13-25
Yasuo Tabei, Daisuke Okanohara, Shuichi Hirose, Koji Tsuda:
LGM: Mining Frequent Subgraphs from Linear Graphs. 26-37
Yasuhiro Fujiwara, Makoto Onizuka, Masaru Kitsuregawa:
Efficient Centrality Monitoring for Time-Evolving Graphs. 38-50
Rajul Anand, Chandan K. Reddy:
Graph-Based Clustering with Constraints. 51-62

Jing Yang, Lian Li:
A Partial Correlation-Based Bayesian Network Structure Learning Algorithm under SEM. 63-74
Rohit Parimi, Doina Caragea:
Predicting Friendship Links in Social Networks Using a Topic Modeling Approach. 75-86
Chao Li, Zhongying Zhao, Jun Luo, Jianping Fan:
Info-Cluster Based Regional Influence Analysis in Social Networks. 87-98
Richi Nayak:
Utilizing Past Relations and User Similarities in a Social Matching System. 99-110
Jhao-Yin Li, Mi-Yen Yeh:
On Sampling Type Distribution from Heterogeneous Social Networks. 111-122
Di Jin, Dayou Liu, Bo Yang, Carlos Baquero, Dongxiao He:
Ant Colony Optimization with Markov Random Walk for Community Detection in Graphs. 123-134

Wei Luo, Marcus Gallagher:
Faster and Parameter-Free Discord Search in Quasi-Periodic Time Series. 135-148
Krisztian Buza, Alexandros Nanopoulos, Lars Schmidt-Thieme:
INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification. 149-160
Harya Widiputra, Russel Pears, Nikola Kasabov:
Multiple Time-Series Prediction through Multiple Time-Series Relationships Profiling and Clustered Recurring Trends. 161-172
Michal Lewandowski, Dimitrios Makris, Jean-Christophe Nebel:
Probabilistic Feature Extraction from Multivariate Time Series Using Spatio-Temporal Constraints. 173-184

Yasuhiro Urabe, Kenji Yamanishi, Ryota Tomioka, Hiroki Iwai:
Real-Time Change-Point Detection Using Sequentially Discounting Normalized Maximum Likelihood Coding. 185-197
Yan Zhou, W. Meador Inge, Murat Kantarcioglu:
Compression for Anti-Adversarial Learning. 198-209
Muhammad Muzammal, Rajeev Raman:
Mining Sequential Patterns from Probabilistic Databases. 210-221
Xu Sun, Hisashi Kashima, Ryota Tomioka, Naonori Ueda:
Large Scale Real-Life Action Recognition Using Conditional Random Fields with Stochastic Training. 222-233
Atsuyoshi Nakamura, Mineichi Kudo:
Packing Alignment: Alignment for Sequences of Various Length Events. 234-245

Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma:
Multiple Distribution Data Description Learning Algorithm for Novelty Detection. 246-257
Hao Huang, Qinming He, Jiangfeng He, Lianhang Ma:
RADAR: Rare Category Detection via Computation of Boundary Degree. 258-269
Jun Gao, Weiming Hu, Zhongfei (Mark) Zhang, Xiaoqin Zhang, Ou Wu:
RKOF: Robust Kernel-Based Local Outlier Detection. 270-283
Flora S. Tsai, Yi Zhang:
Chinese Categorization and Novelty Mining. 284-295
Timothy M. Hospedales, Shaogang Gong, Tao Xiang:
Finding Rare Classes: Adapting Generative and Discriminative Models in Active Learning. 296-308

Xiannian Fan, Ke Tang, Thomas Weise:
Margin-Based Over-Sampling Method for Learning from Imbalanced Datasets. 309-320
Yuxuan Li, Xiuzhen Zhang:
Improving k Nearest Neighbor with Exemplar Generalization for Imbalanced Classification. 321-332
Pengyi Yang, Zili Zhang, Bing Bing Zhou, Albert Y. Zomaya:
Sample Subset Optimization for Classifying Imbalanced Biological Data. 333-344
Wei Liu, Sanjay Chawla:
Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets. 345-356

Maya Wardeh, Frans Coenen, Trevor J. M. Bench-Capon, Adam Zachary Wyner:
Multi-agent Based Classification Using Argumentation from Experience. 357-369
Chao Luo, Yanchang Zhao, Dan Luo, Chengqi Zhang, Wei Cao:
Agent-Based Subspace Clustering. 370-381

Tias Guns, Siegfried Nijssen, Luc De Raedt:
Evaluating Pattern Set Mining Strategies in a Constraint Programming Framework. 382-394
Jun Du, Charles X. Ling:
Asking Generalized Queries with Minimum Cost. 395-406
Pei Li, Jeffrey Xu Yu, Hongyan Liu, Jun He, Xiaoyong Du:
Ranking Individuals and Groups by Influence Propagation. 407-419
Yifeng Zeng, Xian He, Yanping Xiang, Hua Mao:
Dynamic Ordering-Based Search Algorithm for Markov Blanket Discovery. 420-431
Cláudio Rebelo de Sá, Carlos Soares, Alípio Mário Jorge, Paulo J. Azevedo, Joaquim Pinto da Costa:
Mining Association Rules for Label Ranking. 432-443
Stephan Günnemann, Hardy Kremer, Charlotte Laufkötter, Thomas Seidl:
Tracing Evolving Clusters by Subspace and Value Similarity. 444-456
Ghita Berrada, Ander de Keijzer:
An IFS-Based Similarity Measure to Index Electroencephalograms. 457-468
Aditya Desai, Himanshu Singh, Vikram Pudi:
DISC: Data-Intensive Similarity Measure for Categorical Data. 469-481
Ning Gao, Zhi-Hong Deng, Hang Yu, Jia-Jian Jiang:
ListOPT: Learning to Optimize for XML Ranking. 482-492
Marc Segond, Christian Borgelt:
Item Set Mining Based on Cover Similarity. 493-505

Bo Wang, Zhaonan Li, Jie Tang, Kuo Zhang, Songcan Chen, Liyun Ru:
Learning to Advertise: How Many Ads Are Enough? 506-518
Colin DeLong, Nishith Pathak, Kendrick Erickson, Eric Perrino, Kyong Jin Shim, Jaideep Srivastava:
TeamSkill: Modeling Team Chemistry in Online Multi-player Games. 519-531
Dan He, D. Stott Parker:
Learning the Funding Momentum of Research Projects. 532-543
Yi Guo, Junbin Gao:
Local Feature Based Tensor Kernel for Image Manifold Learning. 544-554

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