Representation Learning

• Autoencoders, Wikipedia page, A tutorial. There are many other resources on the web that you can use. (Chosen by Pedro Casas and Daniel Parreira)
• Carl Doersch, Tutorial on Variational Autoencoders, arXiv:1606.05908v2 (Chosen by Jason Yu and Rajshree Daulatabad)
• Tomas Mikolov, et al., Distributed Representations of Words and Phrases and their Compositionality, NIPS 2013.

Outlier/Anomaly Detection

• Md Amran Siddiqui, et al., Feedback-Guided Anomaly Discovery via Online Optimization, KDD 2018
• Emaad Ahmed Manzoor, et al., xStream: Outlier Dete?x?ion in Feature-Evolving Data Streams, KDD 2018. (Chosen by Hoorieh Marefat and Sara Akhavan)
• Lei Cao, Mingrui Wei, Di Yang, Elke A. Rundensteiner Online Outlier Exploration Over Large Datasets, Proceedings of KDD'15, Sydney, NSW, Australia, 2015.
• Alban Siffer, et al., Anomaly Detection in Streams with Extreme Value Theory, Proceedings of KDD'17.
• Chong Zhou and Randy C. Pa?enroth, Anomaly Detection with Robust Deep Autoencoders, Proceedings of KDD'17 (Chosen by Yoon Tae Kim)

Association, Frequent Pattern, Emerging Pattern, and High Utility Pattern Mining

• Scalable Techniques for Mining Causal Structures, Craig Silverstein, Rajeev Motwani, Sergey Brin, and Jeff D. Ullman, Proceedings of the 24th International Conference on Very Large Data Bases (VLDB), 1998
• Xindong Wu, Chengqi Zhang and Shichao Zhang, Efficient Mining of Both Positive and Negative Association Rules. ACM Transactions on Information Systems, 22(2004), 3: 381-405. (SCI).
• Guozhu Dong and Jinyan Li Efficient Mining of Emerging Patterns: Discovering Trends and Differences, KDD 1999: 43-52.
• Hongjian Fan and Kotagiri Ramamohanarao, Efficiently Mining Interesting Emerging Patterns, Proceedings of WAIM, 2003.
• Jiong Yang, Wei Wang, Philip S. Yu: Infominer: mining surprising periodic patterns. KDD 2001: 395-400
• M. Liu and J. Qu. Mining high utility itemsets without candidate generation, Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM '12, pages 55-64, New York, NY, USA, 2012.
• Vincent S. Tseng, Cheng-Wei Wu1, Bai-En Shie, and Philip S. Yu, UP-Growth: An Efficient Algorithm for High Utility Itemset Mining, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pages 253-262, ACM New York, NY, USA, 2010.

Decision Tree Learning

• Chaitanya Manapragada, Geoffrey I. Webb and Mahsa Salehi, Extremely Fast Decision Tree, KDD 2018.
• Q. Yang, J. Yin, C. X. Ling and R. Pan, Extracting Actionable Knowledge from Decision Trees, IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 19(1). 43-56, 2007. (Chosen by Pierre Duez)
• Johannes Gehrke , Raghu Ramakrishnan , Venkatesh Ganti. RainForest: A framework for fast decision tree construction of large datasets, In VLDB'98, pp. 416-427, New York, NY, 1998.
• Learning Trees and Rules with Set-valued Features, William W. Cohen, Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), 1996.
• Cesar Ferri, Peter Flach and Jose Hernandez-Orallo, Learning Decision Trees Using the Area Under the ROC Curve, Proceedings of the 19th International Conference on Machine Learning, Morgan Kaufmann, July 2002, pp.139-146.

Decision Rule Learning

• Quinlan, J. R. and Cameron-Jones, R. M. FOIL: A Midterm Report. Proc. of ECML, Vienna, Austria, 1993. pp3-20.
• Frederic Stahla1 and Max Bramer, Scaling up classification rule induction through parallel processing, Knowledge Engineering Review, Vol.28, No.4, December 2013. (Chosen by Melissa Kremer)

Support Vector Machines

• B. Boser, I. Guyon and V.N. Vapnik. A Training Algorithm for Optimal Margin Classifiers, Proc. of Fifth Annual Workshop on Computational Learning Theory, pp.114-152, 1992.
• Bin Gu, et al., New Incremental Learning Algorithm for Semi-Supervised Support Vector Machine, KDD 2018.

Bayesian Network Learning

• Gregory F. Cooper, Edward Herskovits, A Bayesian method for the induction of probabilistic networks from data, Machine Learning, Vol.9, No.4, 1992, pp.309-347.

(Deep) Neural Networks

• Hugo Larochelle, Yoshua Bengio, Jerome Louradour and Pascal Lamblin, Exploring Strategies for Training Deep Neural Networks, Journal of Machine Learning Research 1 (2009) 1-40. (Chosen by Hossein Pourmodheji)
• Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS'12). (Chosen by Faizaan and Andy Jung)
• Kaiming He, et al., Deep Residual Learning for Image Recognition, arXiv:1512.03385, 2015. (Chosen by Kang Zhao and Yar Rouf)
• Teuvo Kohonen, Self-Organizing Map (SOM), 1982.
• Sepp Hochreiter and Jurgen Schmidhuber, Long Short-Term Memory, 9(8):1735{1780, 1997
• Jung-Woo Ha, Hyuna Pyo and Jeonghee Kim, Large-Scale Item Categorization in e-Commerce Using Multiple Recurrent Neural Networks, Proceedings of KDD'16, 2016. (Chosen by Sadia Chowdhury and Md Tahmid Rahman Laskar)

Clustering

• Alessandro Epasto, et al., Ego-Splitting Framework: from Non-Overlapping to Overlapping Clusters, Proceedings of KDD'17.
• David Hallac, et al., Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data, Proceedings of KDD'17.
• Kiri Wagstaff, Claire Cardie, Seth Rogers and Stefan Schroedl, Constrained K-means Clustering with Background Knowledge, Proceedings of the Eighteenth International Conference on Machine Learning, 2001, p. 577-584.
• CACTUS-Clustering Categorical Data Using Summaries, Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishnan, Proc. 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), 1999 Aug, pp. 73-83.
• ROCK: A Robust Clustering Algorithm for Categorical Attributes, Sudipto Guha, Rajeev Rastogi, Kyuseok Shim, Proceedings of the 15th International Conference on Data Engineering, 23-26 March 1999, Sydney, Austrialia, IEEE CS Press, 1999, pp. 512-521.
• BIRCH: an efficient data clustering method for very large databases, Tian Zhang, Raghu Ramakrishnan, Miron Livny, Proceedings of the 1996 ACM SIGMOD international conference on Management of data , 1996, pp. 103-114.
• CURE: An Efficient Clustering Algorithm for Large Databases, Sudipto Guha, Rajeev Rastogi, Kyuseok Shim, Proceedings of the ACM SIGMOD Conference, 1998.

Data Stream Mining

• Frequent item(set) Mining
• Approximate Frequency Counts over Data Streams, by Gurmeet Singh Manku, Rajeev Motawani, in the International Conference on Very Large Data Bases (VLDB) 2002.
• M. Charikar, K. Chen and M. Farach-Colton. Finding Frequent Items in Data Streams. International Colloquium on Automata,Languages, and Programming (ICALP '02) 508--515.
• Finding Recent Frequent Itemsets Adaptively over Online Data Streams, by Joong Hyuk Chang, Won Suk Lee, in the ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) 2003.
• Classification
• On Demand Classification of Data Streams, Aggarwal, Han, Wang, and Yu, KDD'04.
• Mining Time-Changing Data Streams, by Geoff Hulten, Laurie Spencer, Pedro Domingos, in the ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) 2001.
• F. Ferrer-Troyano, J. Aguilar-Ruiz and J. Riquelme, Incremental Rule Learning and Border Examples Selection from Numerical Data Streams, J. of Universal Computer Science,11(8), 2005.
• M. Maloof and R. Michalski, Incremental learning with partial instance memory, Artificial Intelligence, Vol.154, Issue 1-2, April 2004.
• H. Wang, W. Fan, P. Yu and J. Han. Mining Concept-drifting Data Streams Using Ensemble Classifiers, Proceedings of ACM SIGKDD Conference, 2003.
• D. Sotoudeh and A. An. Partial Drift Detection Using a Rule Induction Framework, Proceedings of the 19th ACM International Conference on Information and Knowledge Management, Toronto, Canada, October 26-30, 2010.
• Clustering
• Charu C. Aggarwal, Jiawei Han, Jianyong Wang, Philip S. Yu. A Framework for Clustering Evolving Data Streams Proceedings of the International Conference on Very Large Data Bases (VLDB) 2003.
• Konstantinos Kalpakis, Dhiral Gada, and Vasundhara Puttagunta, Distance Measures for Effective Clustering of ARIMA Time Series, ICDM'01.
• Concept Drift Detection
• Tamraparni Dasu, Shankar Krishnan, Suresh Venkatasubramanian, and Ke Yi, An Information-Theoretic Approach to Detecting Changes in Multi-Dimensional Data Streams, Proceedings of the 38th Symposium on the Interface of Statistics, Computing Science, and Applications, pages 1-24, 2006.
• P. Vorburg and A. Bernstein. Entropy-based concept shift detection. Proceedings of the Sixth International Conference on Data Mining, pages 1113-1118, 2006.
• Evaluation
  • Albert Bifet, Gianmarco de Francisci Morales, Jesse Read, Geoff Holmes, Bernhard Pfahringer Efficient Online Evaluation of Big Data Stream Classifiers, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, 2015.

Graph or Social Network Mining

• Jian Tang, et al., LINE: Large-scale Information Network Embedding, Proceedings of WWW'15, Florence, Italy, 2015.
• Aditya Grover and Jure Leskovec, node2vec: Scalable Feature Learning for Networks, Proceedings of KDD'16. (Chosen by Saim Mehmood and Ahmadreza Jeddi)
• Bryan Perozzi, DeepWalk: Online Learning of Social Representations, Proceedings of KDD/14.
• Hao Yin, et al., Local Higher-Order Graph Clustering, Proceedings of KDD'17.
• Manuel Gomez-Rodriguez, Jure Leskovec and Andreas Krause, Inferring Networks of Diffusion and Influence, ACM Transactions on Knowledge Discovery from Data, Vol. 5, No. 4, February 2012.
• Jie Tang, Jimeng Sun, Chi Wang and Zi Yang, Social Influence Analysis in Large-scale Networks, Proceedings of the Fifteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD'09), 2009.
• D. Crandall, et al., Feedback Effects between Similarity and Social Influence in Online Communities, Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD'08), 2008.
• Jaewon Yang, Julian McAuley and Jure Leskovec, Community Detection in Networks with Node Attributes, Proceedings of ICDM, 2013.
• Ziwei Zhang, et al., Arbitrary-Order Proximity Preserved Network Embedding, KDD 2018.

Text Mining

• Quoc Le and Tomas Mikolov, Distributed Representations of Sentences and Documents, Proceedings of The 31st International Conference on Machine Learning, 2014. (Chosen by Marjan Delpisheh and Nahid Alimohammadi)

Topic Detection and Tracking

• D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent dirichlet allocation, J. Mach. Learn. Res., vol. 3, pp. 993?1022, 2003.
• D. Blei, J. McAuliffe. . Neural Information Processing Systems 21, 2007
• X. Wang and A. McCallum, Topics over time: a non-markov continuous-time model of topical trends, in Proceedings of the 12th ACM SIGKDD, 2006, pp. 424?433.
• C. Wang, D. Blei, and D. Heckerman. Continuous time dynamic topic models. In Uncertainty in Artificial Intelligence [UAI], 2008.
• L. AlSumait, D. Barbara, and C. Domeniconi, On-line LDA: Adaptive topic models for mining text streams with applications to topic detection and tracking, in Proceedings of the 8th IEEE ICDM, 2008, pp.3?12.
• Zhiyuan Chen and Bing Liu, Mining Topics in Documents: Standing on the Shoulders of Big Data, Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014.

Event Detection and Tracking

• Polina Rozenshtein, et al., Event Detection in Activity Networks, Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014.
• Maximilian Walther and Michael Kaisser, Geo-spatial Event Detection in the Twitter Stream, ECIR, 2013.
• Feng Chen and Daniel B. Neill, Non-Parametric Scan Statistics for Event Detection and Forecasting in Heterogeneous Social Media Graphs, Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014.
• Chen Luo, et al., Correlating Events with Time Series for Incident Diagnosis, Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014.
• Mikalai Tsytsarau, et al., Dynamics of News Events and Social Media Reaction, Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014.
• Tim Althoff, Xin Luna Dong, Kevin Murphy, Safa Alai, Van Dang, Wei Zhang, TimeMachine: Timeline Generation for Knowledge-Base Entities, Sydney, NSW, Australia, 2015.
• Jakub Piskorski, Hristo Tanev, Martin Atkinson, Eric van der Goot, Vanni Zavarella, Online News Event Extraction for Global Crisis Surveillance, Lecture Notes in Computer Science, Volume 6910, 2011.

Opinion Mining (Sentiment Analysis)

• Yequan Wang, Aixin Sun, Jialong Han, Ying Liu and Xiaoyan Zhu, Sentiment Analysis by Capsules, WWW 2018.
• Lin Gong, Hongning Wang, When Sentiment Analysis Meets Social Network: A Holistic User Behavior Modeling in Opinionated Data, KDD 2018.
• C. Tao et al., User-Level Sentiment Analysis Incorporating Social Networks, KDD'11, 2011.
• Murthy Ganapathibhotla and Bing Liu. Mining Opinions in Comparative Sentences. Proceedings of the 22nd International Conference on Computational Linguistics (Coling-2008), Manchester, 18-22 August, 2008.
• Xiaowen Ding, Bing Liu and Philip S. Yu. A Holistic Lexicon-Based Appraoch to Opinion Mining. Proceedings of First ACM International Conference on Web Search and Data Mining (WSDM-2008), Feb 11-12, 2008, Stanford University, Stanford, California, USA.
• Yu, X., Liu, Y., Huang, X. and An, A., Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain , IEEE Transactions on Knowledge and Data Engineering (TKDE), 24(4): 720-734, 2012.

User Behavior Modeling/Profiling

• Meizi Zhou, Zhuoye Ding, Jiliang Tang, Dawei Yin, Micro Behaviors: A New Perspective in E-commerce Recommender Systems, WSDM 2018.
• Ning Su, et al., User Intent, Behaviour, and Perceived Satisfaction in Product Search, WSDM 2018.
• Golnoosh Farnadi, et al., User Profiling through Deep Multimodal Fusion, WSDM 2018. (Chosen by Ali Jalalifar and Shadi Sadeghpour)

Learning from Imbalanced Datasets

• PNrule: A New Framework for Learning Classifier Models in Data Mining (A Case-Study in Network Intrusion Detection), Ramesh Agarwal and Mahesh V. Joshi, 2001.
• Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P, SMOTE: Synthetic Minority Over-sampling TEchnique, Journal of Artificial Intelligence Research, 16, 2002, 341-378. (Chosen by Bo Wang)
• Siong Thye Goh and Cynthia Rudin, Box Drawings for Learning with Imbalanced Data, Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014.

Parallal and Distributed Data Mining for Big Data with MapReduce

• Haoyuan Li, et al., PFP: Parallel FP-Growth for Query Recommendation, Proceedings of the 2008 ACM conference on Recommender systems, 2008.

Recommender Systems

• Gediminas Adomavicius and Alexander Tuzhilin, Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, IEEE Transactions on Knowledge and Data Engineering, Vol.17, No.6, June 2005.
• Jinoh Oh, Wook-Shin Han, Hwanjo Yu, Xiaoqian Jiang, Fast and Robust Parallel SGD Matrix Factorization, Sydney, NSW, Australia, 2015.
• Antonino Freno, Martin Saveski, Rodolphe Jenatton, C?ric Archambeau, One-Pass Ranking Models for Low-Latency Product Recommendations, Sydney, NSW, Australia, 2015.
• Konstantina Christakopoulou, Filip Radlinski and Katja Hofmann, Towards Conversational Recommender Systems, Proceedings of KDD'16, 2016.