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Accepted paper

Regular papers | Short Papers Paper ID Paper Title Author DM218 Indexing and Mining One Billion Time Series Alessandro Camerra, jin shieh, Themis Palpanas, and Eamonn Keogh DM240 Modeling Information Diffusion in Social Media Jaewon Yang and Jure Leskovec DM248 Clustering Large Attributed Graphs: An Efficient Incremental Approach Yang Zhou, Hong Cheng, and Jeffrey Xu Yu DM251 minCEntropy: a Novel Information Theoretic Approach for the Generation of Alternative Clusterings Xuan Vinh Nguyen and Julien Epps DM263 A Graph-Based Approach for Multi-Folder Email Classification sharma chakravarthy, aravind venkatachalam, and aditya telang DM294 Heuristic best-first search in separation of interleaved Web sessions Marko Pozenel, Viljan Mahnic, and Matjaž Kukar DM315 Discovering Correlated Subspace Clusters in 3D Continuous-Valued Data Kelvin Sim, Vivekanand Gopalkrishnan, and Zeyar Aung DM317 Co-clustering of Lagged Data Eran Shaham, David Sarne, and Boaz Ben-Moshe DM319 An Approach Based on Tree Kernels for Opinion Mining of Online Product Reviews Peng Jiang, Chunxia Zhang, Hongping Fu, Zhendong Niu, and Qing Yang DM326 An Unsupervised Approach to Modeling Personalized Contexts of Mobile Users Tengfei Bao, Happia Cao, Enhong Chen, Jilei Tian, and Hui Xiong DM342 Abstraction Augmented Markov Models Cornelia Caragea, Adrian Silvescu, Doina Caragea, and Vasant Honavar DM352 A Novel Contrast Co-Learning Framework For Generating High Quality Training Data zeyu zheng, Jun Yan, and Simon yan DM356 Adaptive Distances on Sets of Vectors Adam Woznica and Alexandros Kalousis DM361 Mining Sensor Streams for Discovering Human Activity Patterns Over Time Parisa Rashidi and Diane Cook DM367 A Binary Decision Diagram-Based One-Class Classifier Takuro Kutsuna DM379 Training Conditional Random Fields Using Transfer Learning for Gesture Recognition Jie Liu DM380 Viral Marketing for Multiple Products Anirban Majumder, Samik Datta, and Nisheeth Shrivastava DM401 Active Learning from Multiple Noisy Labelers with Varied Costs Yaling Zheng, Stephen Scott, and Kun Deng DM438 Bayesian Maximum Margin Clustering Bo Dai, Baogang Hu, and Gang Niu DM445 Bayesian Aggregation of Binary Classifiers Sunho Park and Seungjin Choi DM452 Multi-agent random walks for local clustering on graphs Morteza Alamgir and Ulrike von Luxburg DM455 Learning Attribute-to-Feature Mappings for Cold-start Recommendations Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Steffen Rendle, and Lars Schmidt-Thieme DM459 Subgroup Discovery meets Bayesian networks – an Exceptional Model Mining approach Wouter Duivesteijn, Arno Knobbe, Ad Feelders, and Matthijs van Leeuwen DM472 Exploiting Unlabeled Data to Enhance Ensemble Diversity Min-Ling Zhang and Zhi-Hua Zhou DM494 Mining Historical Manuscripts with Local Color Patches Qiang Zhu and Eamonn Keogh DM504 An Extensive Empirical Study on Semi-supervised Learning Yuanyuan Guo and Harry Zhang DM508 gSkeletonClu: Density-based Network Clustering via Structure-Connected Tree Division or Agglomeration Heli Sun, Jianbin Huang, Jiawei Han, Hongbo Deng, Peixiang Zhao, and Boqin Feng DM521 Discovering Overlapping Groups in Social Media Xufei Wang, Lei Tang, Huiji Gao, and Huan Liu DM526 Spatiotemporal Event Detection in Mobility Network Tom Au, Rong Duan, Heeyoung Kim, and Guang Qin Ma DM530 Improved Consistent Sampling Algorithm for Weighted Minhash Computation, Retrieval and Sketching under Jaccard and L1 Metrics Sergey Ioffe DM539 Improving Kernel Methods through Complex Data Mapping Hang Zhou, Fabio Ramos, and Eric Nettleton DM547 Polishing the Right Apple: Anytime Classification Also Benefits Data Streams with Constant Arrival Times jin shieh and Eamonn Keogh DM555 Detecting Blackholes and Volcanoes in Directed Networks Zhongmou Li, Hui Xiong, and Yanchi Liu DM572 Exponential Family Tensor Factorization for Missing-Values Prediction and Anomaly Detection Kohei Hayashi, Takashi Takenouchi, Tomohiro Shibata, Yuki Kamiya, Daishi Kato, Kazuo Kunieda, Keiji Yamada, and Kazushi Ikeda DM586 Exploiting Local Data Uncertainty to Boost Global Outlier Detection Bo Liu, Jie Yin, Yanshan Xiao, Longbing Cao, and Philip Yu DM592 Generalised Mass Estimation and Mass-based Clustering Kai Ming Ting and Jonathan Wells DM596 NESVM: a Fast Gradient Method for Support Vector Machines Tianyi Zhou, Dacheng Tao, and Xindong Wu DM597 CLUSMASTER: A Clustering Approach for Sampling Data Streams in Sensor Networks Alzennyr Da Silva, Raja Chiky, and Georges Hebrail DM602 D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-Defined Classification Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, and Zhongzhi Shi DM617 Finding Local Anomalies in Very High Dimensional Space Timothy de Vries, Sanjay Chawla, and Michael Houle DM634 A Pairwise-Systematic Microaggregation for Statistical Disclosure Control Md Enamul Kabir, Hua Wang, and Yanchun Zhang DM636 Efficient discovery of the top-K optimal dependency rules with the Fisher's exact test of significance Wilhelmiina Hämäläinen DM642 Decision trees for uplift modeling Szymon Jaroszewicz and Piotr Rzepakowski DM651 Mining Closed Strict Episodes Nikolaj Tatti and Boris Cule DM668 Adding Latent Features to a Log-Linear Model for Dyadic Prediction Aditya Menon and Charles Elkan DM670 Network Simplification with Minimal Loss of Connectivity Fang Zhou, Sébastien Mahler, and Hannu Toivonen DM676 Scalable Influence Maximization in Social Networks under the Linear Threshold Model Wei Chen, Yifei Yuan, and Li Zhang DM683 PGLCM: Efficient Parallel Mining of Closed Frequent Gradual Itemsets Trong Dinh Thac Do, Anne Laurent, and Alexandre Termier DM704 Feature Selection for Unsupervised Learning using Random Cluster Ensembles Haytham Elghazel and Alex Aussem DM708 LogTree: A Framework for Generating System Events from Raw Textual Logs Liang Tang and Tao Li DM714 Stratified Sampling for Data Mining on the Deep Web Tantan Liu, Fan Wang, and Gagan Agrawal DM720 Rare Category Characterization Jingrui He, Hanghang Tong, and Jaime Carbonell DM731 Constraint Based Dimension Correlation and Distance Divergence for Clustering High-Dimensional Data Xianchao Zhang, Yao Wu, and Yang Qiu DM757 Weighted Feature Subset Non-Negative Matrix Factorization and its Applications to Document Understanding Dingding Wang, Chris Ding, and Tao Li DM759 Multi-Document Summarization Using Minimum Distortion Tengfei Ma and Xiaojun Wan DM762 Fast and Flexible Multivariate Time Series Subsequence Search Kanishka Bhaduri, Qiang Zhu, Nikunj Oza, and Ashok Srivastava DM782 Multi-Label Feature Selection for Graph Classification Xiangnan Kong and Philip Yu DM824 Edge Weight Regularization Over Multiple Graphs For Similarity Learning Pradeep Muthukrishnan, Dragomir Radev, and Qiaozhu Mei DM825 Consequences of Variability in Classifier Performance Estimates Troy Raeder, T. Ryan Hoens, and Nitesh Chawla DM830 Algorithm for Discovering Low-Variance 3-Clusters From Real-Valued Datasets Zhen Hu and Raj Bhatnagar DM837 A Conscience On-line Learning Approach for Kernel-Based Clustering Chang-Dong Wang, Jian-Huang Lai, and Jun-Yong Zhu DM846 Active Spectral Clustering Xiang Wang and IAN DAVIDSON DM861 A New SVM Approach to Multi-Instance Multi-Label Learning Nam Nguyen DM876 Learning a Bi-Stochastic Data Similarity Matrix Fei Wang, Ping Li, and Christian Konig DM880 Detecting Novel Discrepancies in Communication Networks James Abello, Tina Eliassi-Rad, and Nishchal Devanur DM882 Sequential Latent Dirichlet Allocation: Discover Underlying Topic Structures within a Document Lan Du, Wray Buntine, and Huidong Jin DM883 Term Filtering with Bounded Error Zi Yang, Wei Li, Jie Tang, and Juanzi Li DM907 SMILE: A Similarity-Based Approach for Multiple Instance Learning Yanshan Xiao, Bo Liu, Longbing Cao, Jie Yin, and Xindong Wu DM930 Towards Structural Sparsity: An Explicit $\ell_2/\ell_0$ Approach Dijun Luo, Chris Ding, and Heng huang DM938 Permutations as angular data: efficient inference in factorial spaces Sergey Plis, Terran Lane, and Vince Calhoun DM949 A Variance Reduction Framework for Stable Feature Selection Yue Han and Lei Yu DM986 Learning Markov Network Structure with Decision Trees Daniel Lowd and Jesse Davis

 

Data Sciences and Knowledge Discovery Lab Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology, Sydney PO Box 123, Broadway NSW 2007, Australia

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