Advances in Knowledge Discovery and Data Mining: Part 2


Advances in Knowledge Discovery and Data Mining: Part 2 (2011) .. PAKDD 2011, Shenzhen


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

Social Network/Online Analysis

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

Time Series Analysis

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

Sequence Analysis

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

Outlier Detection

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

Imbalanced Data Analysis

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

Agent Mining

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

Evaluation (Similarity, Ranking, Query)

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

Applications

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