10. ICDM 2010: Sydney, Australia

Keynote Abstracts

• Christos Faloutsos:
  Mining Billion-node Graphs: Patterns, Generators and Tools. 5
  
• Geoffrey J. McLachlan:
  Assessing the Significance of Groups in High-Dimensional Data. 6
  
• Xindong Wu:
  10 Years of Data Mining Research: Retrospect and Prospect. 7
  

Regular Papers

• James Abello, Tina Eliassi-Rad, Nishchal Devanur:
  Detecting Novel Discrepancies in Communication Networks. 8-17
  
• Morteza Alamgir, Ulrike von Luxburg:
  Multi-agent Random Walks for Local Clustering on Graphs. 18-27
  
• Tom S. Au, Rong Duan, Heeyoung Kim, Guangqin Ma:
  Spatiotemporal Event Detection in Mobility Network. 28-37
  
• Tengfei Bao, Happia Cao, Enhong Chen, Jilei Tian, Hui Xiong:
  An Unsupervised Approach to Modeling Personalized Contexts of Mobile Users. 38-47
  
• Kanishka Bhaduri, Qiang Zhu, Nikunj C. Oza, Ashok N. Srivastava:
  Fast and Flexible Multivariate Time Series Subsequence Search. 48-57
  
• Alessandro Camerra, Themis Palpanas, Jin Shieh, Eamonn J. Keogh:
  iSAX 2.0: Indexing and Mining One Billion Time Series. 58-67
  
• Cornelia Caragea, Adrian Silvescu, Doina Caragea, Vasant Honavar:
  Abstraction Augmented Markov Models. 68-77
  
• Sharma Chakravarthy, Aravind Venkatachalam, Aditya Telang:
  A Graph-Based Approach for Multi-folder Email Classification. 78-87
  
• Wei Chen, Yifei Yuan, Li Zhang:
  Scalable Influence Maximization in Social Networks under the Linear Threshold Model. 88-97
  
• Alzennyr Da Silva, Raja Chiky, Georges Hébrail:
  CLUSMASTER: A Clustering Approach for Sampling Data Streams in Sensor Networks. 98-107
  
• Bo Dai, Baogang Hu, Gang Niu:
  Bayesian Maximum Margin Clustering. 108-117
  
• Samik Datta, Anirban Majumder, Nisheeth Shrivastava:
  Viral Marketing for Multiple Products. 118-127
  
• Timothy de Vries, Sanjay Chawla, Michael E. Houle:
  Finding Local Anomalies in Very High Dimensional Space. 128-137
  
• Trong Dinh Thac Do, Anne Laurent, Alexandre Termier:
  PGLCM: Efficient Parallel Mining of Closed Frequent Gradual Itemsets. 138-147
  
• Lan Du, Wray Lindsay Buntine, Huidong Jin:
  Sequential Latent Dirichlet Allocation: Discover Underlying Topic Structures within a Document. 148-157
  
• Wouter Duivesteijn, Arno J. Knobbe, Ad Feelders, Matthijs van Leeuwen:
  Subgroup Discovery Meets Bayesian Networks -- An Exceptional Model Mining Approach. 158-167
  
• Haytham Elghazel, Alex Aussem:
  Feature Selection for Unsupervised Learning Using Random Cluster Ensembles. 168-175
  
• Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme:
  Learning Attribute-to-Feature Mappings for Cold-Start Recommendations. 176-185
  
• Yuanyuan Guo, Xiaoda Niu, Harry Zhang:
  An Extensive Empirical Study on Semi-supervised Learning. 186-195
  
• Wilhelmiina Hamalainen:
  Efficient Discovery of the Top-K Optimal Dependency Rules with Fisher's Exact Test of Significance. 196-205
  
• Yue Han, Lei Yu:
  A Variance Reduction Framework for Stable Feature Selection. 206-215
  
• Kohei Hayashi, Takashi Takenouchi, Tomohiro Shibata, Yuki Kamiya, Daishi Kato, Kazuo Kunieda, Keiji Yamada, Kazushi Ikeda:
  Exponential Family Tensor Factorization for Missing-Values Prediction and Anomaly Detection. 216-225
  
• Jingrui He, Hanghang Tong, Jaime G. Carbonell:
  Rare Category Characterization. 226-235
  
• Zhen Hu, Raj Bhatnagar:
  Algorithm for Discovering Low-Variance 3-Clusters from Real-Valued Datasets. 236-245
  
• Sergey Ioffe:
  Improved Consistent Sampling, Weighted Minhash and L1 Sketching. 246-255
  
• Peng Jiang, Chunxia Zhang, Hongping Fu, Zhendong Niu, Qing Yang:
  An Approach Based on Tree Kernels for Opinion Mining of Online Product Reviews. 256-265
  
• Md. Enamul Kabir, Hua Wang, Yanchun Zhang:
  A Pairwise-Systematic Microaggregation for Statistical Disclosure Control. 266-273
  
• Xiangnan Kong, Philip S. Yu:
  Multi-label Feature Selection for Graph Classification. 274-283
  
• Takuro Kutsuna:
  A Binary Decision Diagram-Based One-Class Classifier. 284-293
  
• Zhongmou Li, Hui Xiong, Yanchi Liu, Aoying Zhou:
  Detecting Blackhole and Volcano Patterns in Directed Networks. 294-303
  
• Bo Liu, Jie Yin, Yanshan Xiao, Longbing Cao, Philip S. Yu:
  Exploiting Local Data Uncertainty to Boost Global Outlier Detection. 304-313
  
• Jie Liu, Kai Yu, Yi Zhang, Yalou Huang:
  Training Conditional Random Fields Using Transfer Learning for Gesture Recognition. 314-323
  
• Tantan Liu, Fan Wang, Gagan Agrawal:
  Stratified Sampling for Data Mining on the Deep Web. 324-333
  
• Daniel Lowd, Jesse Davis:
  Learning Markov Network Structure with Decision Trees. 334-343
  
• Dijun Luo, Chris H. Q. Ding, Heng Huang:
  Towards Structural Sparsity: An Explicit l2/l0 Approach. 344-353
  
• Tengfei Ma, Xiaojun Wan:
  Multi-document Summarization Using Minimum Distortion. 354-363
  
• Aditya Krishna Menon, Charles Elkan:
  A Log-Linear Model with Latent Features for Dyadic Prediction. 364-373
  
• Pradeep Muthukrishnan, Dragomir R. Radev, Qiaozhu Mei:
  Edge Weight Regularization over Multiple Graphs for Similarity Learning. 374-383
  
• Nam Nguyen:
  A New SVM Approach to Multi-instance Multi-label Learning. 384-392
  
• Sunho Park, Seungjin Choi:
  Bayesian Aggregation of Binary Classifiers. 393-402
  
• Sergey M. Plis, Terran Lane, Vince D. Calhoun:
  Permutations as Angular Data: Efficient Inference in Factorial Spaces. 403-410
  
• Marko Pozenel, Viljan Mahnic, Matjaz Kukar:
  Separation of Interleaved Web Sessions with Heuristic Search. 411-420
  
• Troy Raeder, T. Ryan Hoens, Nitesh V. Chawla:
  Consequences of Variability in Classifier Performance Estimates. 421-430
  
• Parisa Rashidi, Diane J. Cook:
  Mining Sensor Streams for Discovering Human Activity Patterns over Time. 431-440
  
• Piotr Rzepakowski, Szymon Jaroszewicz:
  Decision Trees for Uplift Modeling. 441-450
  
• Eran Shaham, David Sarne, Boaz Ben-Moshe:
  Co-clustering of Lagged Data. 451-460
  
• Jin Shieh, Eamonn J. Keogh:
  Polishing the Right Apple: Anytime Classification Also Benefits Data Streams with Constant Arrival Times. 461-470
  
• Kelvin Sim, Zeyar Aung, Vivekanand Gopalkrishnan:
  Discovering Correlated Subspace Clusters in 3D Continuous-Valued Data. 471-480
  
• Heli Sun, Jianbin Huang, Jiawei Han, Hongbo Deng, Peixiang Zhao, Boqin Feng:
  gSkeletonClu: Density-Based Network Clustering via Structure-Connected Tree Division or Agglomeration. 481-490
  
• Liang Tang, Tao Li:
  LogTree: A Framework for Generating System Events from Raw Textual Logs. 491-500
  
• Nikolaj Tatti, Boris Cule:
  Mining Closed Strict Episodes. 501-510
  
• Kai Ming Ting, Jonathan R. Wells:
  Multi-dimensional Mass Estimation and Mass-based Clustering. 511-520
  
• Xuan Vinh Nguyen, Julien Epps:
  minCEntropy: A Novel Information Theoretic Approach for the Generation of Alternative Clusterings. 521-530
  
• Chang-Dong Wang, Jian-Huang Lai, Jun-Yong Zhu:
  A Conscience On-line Learning Approach for Kernel-Based Clustering. 531-540
  
• Dingding Wang, Tao Li, Chris H. Q. Ding:
  Weighted Feature Subset Non-negative Matrix Factorization and Its Applications to Document Understanding. 541-550
  
• Fei Wang, Ping Li, Arnd Christian König:
  Learning a Bi-Stochastic Data Similarity Matrix. 551-560
  
• Xiang Wang, Ian Davidson:
  Active Spectral Clustering. 561-568
  
• Xufei Wang, Lei Tang, Huiji Gao, Huan Liu:
  Discovering Overlapping Groups in Social Media. 569-578
  
• Adam Woznica, Alexandros Kalousis:
  Adaptive Distances on Sets of Vectors. 579-588
  
• Yanshan Xiao, Bo Liu, Longbing Cao, Jie Yin, Xindong Wu:
  SMILE: A Similarity-Based Approach for Multiple Instance Learning. 589-598
  
• Jaewon Yang, Jure Leskovec:
  Modeling Information Diffusion in Implicit Networks. 599-608
  
• Zi Yang, Wei Li, Jie Tang, Juanzi Li:
  Term Filtering with Bounded Error. 609-618
  
• Min-Ling Zhang, Zhi-Hua Zhou:
  Exploiting Unlabeled Data to Enhance Ensemble Diversity. 619-628
  
• Xianchao Zhang, Yao Wu, Yang Qiu:
  Constraint Based Dimension Correlation and Distance Divergence for Clustering High-Dimensional Data. 629-638
  
• Yaling Zheng, Stephen D. Scott, Kun Deng:
  Active Learning from Multiple Noisy Labelers with Varied Costs. 639-648
  
• Zeyu Zheng, Jun Yan, Shuicheng Yan, Ning Liu, Zheng Chen, Ming Zhang:
  A Novel Contrast Co-learning Framework for Generating High Quality Training Data. 649-658
  
• Fang Zhou, Sébastien Mahler, Hannu Toivonen:
  Network Simplification with Minimal Loss of Connectivity. 659-668
  
• Hang Zhou, Fabio T. Ramos, Eric Nettleton:
  Improving Kernel Methods through Complex Data Mapping. 669-678
  
• Tianyi Zhou, Dacheng Tao, Xindong Wu:
  NESVM: A Fast Gradient Method for Support Vector Machines. 679-688
  
• Yang Zhou, Hong Cheng, Jeffrey Xu Yu:
  Clustering Large Attributed Graphs: An Efficient Incremental Approach. 689-698
  
• Qiang Zhu, Eamonn J. Keogh:
  Mother Fugger: Mining Historical Manuscripts with Local Color Patches. 699-708
  
• Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, Zhongzhi Shi:
  D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-defined Classification. 709-718
  

Short Papers

• Mohammad Al Hasan, Hilmi Yildirim, Abhirup Chakraborty:
  SONNET: Efficient Approximate Nearest Neighbor Using Multi-core. 719-724
  
• Suhrid Balakrishnan, Sumit Chopra:
  Two of a Kind or the Ratings Game? Adaptive Pairwise Preferences and Latent Factor Models. 725-730
  
• Ranieri Baraglia, Gianmarco De Francisci Morales, Claudio Lucchese:
  Document Similarity Self-Join with MapReduce. 731-736
  
• Antonio Bella, César Ferri, José Hernández-Orallo, M. José Ramírez-Quintana:
  Quantification via Probability Estimators. 737-742
  
• Xiongcai Cai, Michael Bain, Alfred Krzywicki, Wayne Wobcke, Yang Sok Kim, Paul Compton, Ashesh Mahidadia:
  Learning Collaborative Filtering and Its Application to People to People Recommendation in Social Networks. 743-748
  
• Toon Calders, Calin Garboni, Bart Goethals:
  Approximation of Frequentness Probability of Itemsets in Uncertain Data. 749-754
  
• Andrea Campagna, Rasmus Pagh:
  On Finding Frequent Patterns in Event Sequences. 755-760
  
• Nicolas Cebron:
  Active Improvement of Hierarchical Object Features under Budget Constraints. 761-766
  
• Shing-Kit Chan, Wai Lam:
  Pseudo Conditional Random Fields: Joint Training Approach to Segmenting and Labeling Sequence Data. 767-772
  
• Bo Chen, Wai Lam, Ivor W. Tsang, Tak-Lam Wong:
  Location and Scatter Matching for Dataset Shift in Text Mining. 773-778
  
• Xi Chen, Bing Bai, Yanjun Qi, Qihang Lin, Jaime G. Carbonell:
  Learning Preferences with Millions of Parameters by Enforcing Sparsity. 779-784
  
• Robson Leonardo Ferreira Cordeiro, Fan Guo, Donna S. Haverkamp, James H. Horne, Ellen K. Hughes, Gunhee Kim, Agma J. M. Traina, Caetano Traina Jr., Christos Faloutsos:
  QMAS: Querying, Mining and Summarization of Multi-modal Databases. 785-790
  
• Kamalika Das, Ashok N. Srivastava:
  Block-GP: Scalable Gaussian Process Regression for Multimodal Data. 791-796
  
• Jun Du, Charles X. Ling:
  Active Learning with Human-Like Noisy Oracle. 797-802
  
• Ad Feelders:
  Monotone Relabeling in Ordinal Classification. 803-808
  
• George Giannakopoulos, Themis Palpanas:
  The Effect of History on Modeling Systems' Performance: The Problem of the Demanding Lord. 809-814
  
• Fabian Gieseke, Gabriel Moruz, Jan Vahrenhold:
  Resilient K-d Trees: K-Means in Space Revisited. 815-820
  
• Sertan Girgin, Jérémie Mary, Philippe Preux, Olivier Nicol:
  Advertising Campaigns Management: Should We Be Greedy? 821-826
  
• Ben Goodrich, David W. Albrecht, Peter E. Tischer:
  Accelerating Radius-Margin Parameter Selection for SVMs Using Geometric Bounds. 827-832
  
• Francesco Gullo, Carlotta Domeniconi, Andrea Tagarelli:
  Enhancing Single-Objective Projective Clustering Ensembles. 833-838
  
• Francesco Gullo, Giovanni Ponti, Andrea Tagarelli:
  Minimizing the Variance of Cluster Mixture Models for Clustering Uncertain Objects. 839-844
  
• Stephan Günnemann, Ines Färber, Brigitte Boden, Thomas Seidl:
  Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms. 845-850
  
• Robert Gwadera:
  Multi-stream Join Answering for Mining Significant Cross-Stream Correlations. 851-856
  
• Tsukasa Ishigaki, Takeshi Takenaka, Yoichi Motomura:
  Category Mining by Heterogeneous Data Fusion Using PdLSI Model in a Retail Service. 857-862
  
• Santosh Kabbur, Eui-Hong Han, George Karypis:
  Content-Based Methods for Predicting Web-Site Demographic Attributes. 863-868
  
• Faisal Kamiran, Toon Calders, Mykola Pechenizkiy:
  Discrimination Aware Decision Tree Learning. 869-874
  
• U. Kang, Mary McGlohon, Leman Akoglu, Christos Faloutsos:
  Patterns on the Connected Components of Terabyte-Scale Graphs. 875-880
  
• Brendan Kitts, Liang Wei, Dyng Au, Amanda Powter, Brian Burdick:
  Attribution of Conversion Events to Multi-channel Media. 881-886
  
• Neal Lathia, Jon Froehlich, Licia Capra:
  Mining Public Transport Usage for Personalised Intelligent Transport Systems. 887-892
  
• Guangxia Li, Steven C. H. Hoi, Kuiyu Chang, Ramesh Jain:
  Micro-blogging Sentiment Detection by Collaborative Online Learning. 893-898
  
• Junqiang Liu, Ke Wang:
  Enforcing Vocabulary k-Anonymity by Semantic Similarity Based Clustering. 899-904
  
• Sen Liu, Chaolun Xia, Xiaohong Jiang:
  Efficient Probabilistic Latent Semantic Analysis with Sparsity Control. 905-910
  
• Yanchi Liu, Zhongmou Li, Hui Xiong, Xuedong Gao, Junjie Wu:
  Understanding of Internal Clustering Validation Measures. 911-916
  
• Mingsheng Long, Wei Cheng, Xiaoming Jin, Jianmin Wang, Dou Shen:
  Transfer Learning via Cluster Correspondence Inference. 917-922
  
• Zhengdong Lu, Berkant Savas, Wei Tang, Inderjit S. Dhillon:
  Supervised Link Prediction Using Multiple Sources. 923-928
  
• Mohammad M. Masud, Qing Chen, Latifur Khan, Charu C. Aggarwal, Jing Gao, Jiawei Han, Bhavani M. Thuraisingham:
  Addressing Concept-Evolution in Concept-Drifting Data Streams. 929-934
  
• Pauli Miettinen:
  Sparse Boolean Matrix Factorizations. 935-940
  
• Mario Navas, Carlos Ordonez, Veerabhadran Baladandayuthapani:
  On the Computation of Stochastic Search Variable Selection in Linear Regression with UDFs. 941-946
  
• Vit Niennattrakul, Eamonn J. Keogh, Chotirat Ann Ratanamahatana:
  Data Editing Techniques to Allow the Application of Distance-Based Outlier Detection to Streams. 947-952
  
• Keith Noto, Carla E. Brodley, Donna K. Slonim:
  Anomaly Detection Using an Ensemble of Feature Models. 953-958
  
• Markus Ojala:
  Assessing Data Mining Results on Matrices with Randomization. 959-964
  
• Nishith Pathak, Arindam Banerjee, Jaideep Srivastava:
  A Generalized Linear Threshold Model for Multiple Cascades. 965-970
  
• Daniele Quercia, Neal Lathia, Francesco Calabrese, Giusy Di Lorenzo, Jon Crowcroft:
  Recommending Social Events from Mobile Phone Location Data. 971-976
  
• Romain Quere, Hoel Le Capitaine, N. Fraisseix, Carl Frélicot:
  On Normalizing Fuzzy Coincidence Matrices to Compare Fuzzy and/or Possibilistic Partitions with the Rand Index. 977-982
  
• Han Qin, Dejing Dou, Yue Fang:
  Financial Forecasting with Gompertz Multiple Kernel Learning. 983-988
  
• Matthew J. Rattigan, David Jensen:
  Leveraging D-Separation for Relational Data Sets. 989-994
  
• Steffen Rendle:
  Factorization Machines. 995-1000
  
• Doruk Sart, Abdullah Mueen, Walid A. Najjar, Eamonn J. Keogh, Vit Niennattrakul:
  Accelerating Dynamic Time Warping Subsequence Search with GPUs and FPGAs. 1001-1006
  
• Tim Schlüter, Stefan Conrad:
  An Approach for Automatic Sleep Stage Scoring and Apnea-Hypopnea Detection. 1007-1012
  
• Stephan Seufert, Srikanta J. Bedathur, Julián Mestre, Gerhard Weikum:
  Bonsai: Growing Interesting Small Trees. 1013-1018
  
• Mahdi Shafiei, Hugh Chipman:
  Mixed-Membership Stochastic Block-Models for Transactional Networks. 1019-1024
  
• Hanhuai Shan, Arindam Banerjee:
  Generalized Probabilistic Matrix Factorizations for Collaborative Filtering. 1025-1030
  
• Zhiyong Shen, Ping Luo, Shengwen Yang, Xukun Shen:
  Topic Modeling Ensembles. 1031-1036
  
• Zhiyong Shen, Liang Du, Xukun Shen, Yi-Dong Shen:
  Interval-valued Matrix Factorization with Applications. 1037-1042
  
• Xiaoxiao Shi, Wei Fan, Philip S. Yu:
  Efficient Semi-supervised Spectral Co-clustering with Constraints. 1043-1048
  
• Xiaoxiao Shi, Qi Liu, Wei Fan, Philip S. Yu, Ruixin Zhu:
  Transfer Learning on Heterogenous Feature Spaces via Spectral Transformation. 1049-1054
  
• Vikas Sindhwani, Serhat Selcuk Bucak, Jianying Hu, Aleksandra Mojsilovic:
  One-Class Matrix Completion with Low-Density Factorizations. 1055-1060
  
• Jimeng Sun, Daby Sow, Jianying Hu, Shahram Ebadollahi:
  A System for Mining Temporal Physiological Data Streams for Advanced Prognostic Decision Support. 1061-1066
  
• Xu Sun, Hisashi Kashima, Takuya Matsuzaki, Naonori Ueda:
  Averaged Stochastic Gradient Descent with Feedback: An Accurate, Robust, and Fast Training Method. 1067-1072
  
• Daniel Svonava, Michail Vlachos:
  Visualizing Graphs Using Minimum Spanning Dendrograms. 1073-1078
  
• Lu An Tang, Xiao Yu, Sangkyum Kim, Jiawei Han, Chih-Chieh Hung, Wen-Chih Peng:
  Tru-Alarm: Trustworthiness Analysis of Sensor Networks in Cyber-Physical Systems. 1079-1084
  
• Kilian Thiel, Michael R. Berthold:
  Node Similarities from Spreading Activation. 1085-1090
  
• Hanghang Tong, B. Aditya Prakash, Charalampos E. Tsourakakis, Tina Eliassi-Rad, Christos Faloutsos, Duen Horng Chau:
  On the Vulnerability of Large Graphs. 1091-1096
  
• Niko Vuokko, Petteri Kaski:
  Testing the Significance of Patterns in Data with Cluster Structure. 1097-1102
  
• Fei Wang, Ping Li:
  Compressed Nonnegative Sparse Coding. 1103-1108
  
• Ke Wang, Yabo Xu, Raymond Chi-Wing Wong, Ada Wai-Chee Fu:
  Anonymizing Temporal Data. 1109-1114
  
• Zheng Wang, Yangqiu Song, Changshui Zhang:
  Homotopy Regularization for Boosting. 1115-1120
  
• Jianshu Weng, Ee-Peng Lim, Qi He, Cane Wing-ki Leung:
  What Do People Want in Microblogs? Measuring Interestingness of Hashtags in Twitter. 1121-1126
  
• Raymond Chi-Wing Wong, Ada Wai-Chee Fu, Ke Wang, Yabo Xu, Jian Pei, Philip S. Yu:
  Probabilistic Inference Protection on Anonymized Data. 1127-1132
  
• Jun Wu, Mingyu Lu, Chun-Li Wang:
  Collaborative Learning between Visual Content and Hidden Semantic for Image Retrieval. 1133-1138
  
• Yan Xie, Philip S. Yu:
  Max-Clique: A Top-Down Graph-Based Approach to Frequent Pattern Mining. 1139-1144
  
• Qingyan Yang, Ju Fan, Jianyong Wang, Lizhu Zhou:
  Personalizing Web Page Recommendation via Collaborative Filtering and Topic-Aware Markov Model. 1145-1150
  
• Hwanjo Yu, Sungchul Kim:
  Passive Sampling for Regression. 1151-1156
  
• Jun Yu, Weng-Keen Wong, Rebecca A. Hutchinson:
  Modeling Experts and Novices in Citizen Science Data for Species Distribution Modeling. 1157-1162
  
• Kui Yu, Xindong Wu, Hao Wang, Wei Ding:
  Causal Discovery from Streaming Features. 1163-1168
  
• Chongsheng Zhang, Florent Masseglia, Yves Lechevallier:
  ABS: The Anti Bouncing Model for Usage Data Streams. 1169-1174
  
• Peng Zhang, Xingquan Zhu, Jianlong Tan, Li Guo:
  Classifier and Cluster Ensembles for Mining Concept Drifting Data Streams. 1175-1180
  
• Xianchao Zhang, Yansheng Jiang, Wenxin Liang, Xin Han:
  Graph-Based Semi-supervised Learning with Adaptive Similarity Estimation. 1181-1186
  
• Xiangliang Zhang, Wei Wang, Kjetil Nørvåg, Michèle Sebag:
  K-AP: Generating Specified K Clusters by Efficient Affinity Propagation. 1187-1192
  
• Yuan Zhang, Jie Tang, Jimeng Sun, Yiran Chen, Jinghai Rao:
  MoodCast: Emotion Prediction via Dynamic Continuous Factor Graph Model. 1193-1198
  
• Li Zheng, Tao Li, Chris H. Q. Ding:
  Hierarchical Ensemble Clustering. 1199-1204
  
• Jia Zou, Jing Xiao, Rui Hou, Yanqi Wang:
  Frequent Instruction Sequential Pattern Mining in Hardware Sample Data. 1205-1210
  
• Huisheng Zhu, Peng Wang, Xianmang He, Yujia Li, Wei Wang, Baile Shi:
  Efficient Episode Mining with Minimal and Non-overlapping Occurrences. 1211-1216
  

Tutorials

• Vania Bogorny, Shashi Shekhar:
  Spatial and Spatio-temporal Data Mining. 1217
  
• Jun Huan:
  Knowledge Discovery in Academic Drug Discovery Programs: Opportunities and Challenges. 1218
  
• Eamonn J. Keogh:
  How to Do Good Data Mining Research and Get it Published in Top Venues. 1219
  
• Emmanuel Müller, Stephan Günnemann, Ines Färber, Thomas Seidl:
  Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data. 1220
  

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