Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining Publications

2018

Discovering Urban Travel Demands Through Dynamic Zone Correlation in Location-Based Social Networks
Wangsu Hu, Zijun Yao, Sen Yang, Shuhong Chen, Peter J Jin
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), pp. 88--104, 2018

Health-ATM: A Deep Architecture for Multifaceted Patient Health Record Representation and Risk Prediction
Tengfei Ma*, Cao Xiao*, Fei Wang (* equal contribution)
The SIAM International Conference on Data Mining (SDM 18), 2018

A Probabilistic Hough Transform for Opportunistic Crowd-sensing of Moving Traffic Obstacles
Michiaki Tatsubori, Aisha Walcott-Bryant, Reginald Bryant, John Wamburu
2018 SIAM International Conference on Data Mining (SDM 2018)
Abstract (to appear)

Scalable Spectral Clustering Using Random Binning Features
Wu, Lingfei and Chen, Pin-Yu and Yen, Ian En-Hsu and Xu, Fangli and Xia, Yinglong and Aggarwal, Charu
ACM KDD (oral paper), 2018
Abstract

Distributed Ledger Technology for Document and Workflow Management in Trade and Logistics
Z. Wang, D. Y. Liffman, D. Karunamoorthy, and E. Abebe
ACM CIKM, 2018

Bug Localization by Learning to Rank and Represent Bug Inducing Changes
Pablo Loyola, Kugmoorthy Gajananan, Fumiko Satoh
International Conference on Information and Knowledge Management (CIKM), 2018

E-tail product return prediction via hypergraph-based local graph cut
Jianbo Li, Jingrui He, Yada Zhu
KDD, 2018
Abstract

Recent decades have witnessed the rapid growth of E-commerce. In particular, E-tail has provided customers with great convenience by allowing them to purchase retail products anywhere without visiting the actual stores. A recent trend in E-tail is to allow free shipping and hassle-free returns to further attract online customers. However, a downside of such a customer-friendly policy is the rapidly increasing return rate as well as the associated costs of handling returned online orders. Therefore, it has become imperative to take proactive measures for reducing the return rate and the associated cost. Despite the large amount of data available from historical purchase and return records, up until now, the problem of E-tail product return prediction has not attracted much attention from the data mining community. To address this problem, in this paper, we propose a generic framework for E-tail product return prediction named HyperGo. It aims to predict the customerÿ s intention to return after s/he has put together the shopping basket. For the baskets with a high re- turn intention, the E-tailers can then take appropriate measures to incentivize the customer not to issue a return and/or prepare for reverse logistics. The proposed HyperGo is based on a novel hypergraph representation of historical purchase and return records, effectively leveraging the rich information of basket composition. For a given basket, we propose a local graph cut algorithm using truncated random walk on the hypergraph to identify similar historical baskets. Based on these baskets, HyperGo is able to estimate the return intention on two levels: basket-level vs. product-level, which provides the E-tailers with detailed information regarding the reason for a potential return (e.g., duplicate products with different colors). One major benefit of the proposed local algorithm lies in its time complexity, which is linearly dependent on the size of the output cluster and polylogarithmically dependent on the volume of the hypergraph. This makes HyperGo particularly suitable for processing large-scale data sets. The experimental results on multiple real-world E-tail data sets demonstrate the effectiveness and efficiency of HyperGo.

2017

Ranking based multitask learning of scoring functions
Ivan Stojkovic, Mohamed Ghalwash, Zoran Obradovic
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML), pp. 721--736, 2017

Cost Sensitive Time-series Classification
Shoumik Roychoudhury, Mohamed Ghalwash, Zoran Obradovic
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML), pp. 495--511, 2017

An RNN Architecture with Dynamic Temporal Matching for Personalized Predictions of Parkinson's Disease.
Chao Che*, Cao Xiao*, Jian Liang, Bo Jin, Jiayu Zhou, Fei Wang. (* equal contribution)
SIAM International Conference on Data Mining (SDM 17) , 2017

Computational Drug Discovery with Dyadic Positive-Unlabeled Learning
Yashu Liu, Shuang Qiu, Ping Zhang, Pinghua Gong, Fei Wang, Guoliang Xue, Jieping Ye
SIAM International Conference on Data Mining (SDM), 2017
Abstract

Polyadic Regression and its Application to Chemogenomics
Ioakeim Perros, Fei Wang, Ping Zhang, Peter Walker, Richard Vuduc, Jyotishman Pathak, Jimeng Sun
SIAM International Conference on Data Mining (SDM), 2017
Abstract

Revisiting Spectral Graph Clustering with Generative Community Models
Chen, Pin-Yu and Wu, Lingfei
ICDM (regular paper), 2017
Abstract

Gadei: On scale-up training as a service for deep learning
Wei Zhang, Minwei Feng, Yunhui Zheng, Yufei Ren, Yandong Wang, Ji Liu, Peng Liu, Bing Xiang, Li Zhang, Bowen Zhou, Fei Wang
IEEE International Conference on Data Mining (ICDM), 2017

Multi-task Multi-modal Models for Collective Anomaly Detection
Tsuyoshi Ide, Dzung T. Phan, Jayant Kalagnanam
Proceedings of the 2017 IEEE International Conference on Data Mining (ICDM 17)

A Novel l0-constrained Gaussian Graphical Model for Anomaly Localization
Dzung T. Phan, Tsuyoshi Ide, Jayant Kalagnanam, Matt Menickelly, Katya Scheinberg
Proceedings of the 17th International Conference on Data Mining Workshops (ICDMW 2017), pp. 830-833

Novel Exact and Approximate Algorithms for the Closest Pair Problem
Rajasekaran, Sanguthevar and Saha, Subrata and Cai, Xingyu
Data Mining (ICDM), 2017 IEEE International Conference on, pp. 1045--1050

A Method to Accelerate Human in the Loop Clustering
Anni Coden, Marina Danilevsky, Daniel Gruhl, Linda Kato, and Meena Nagarajan
SDM, 2017

Patient Subtyping via Time-Aware LSTM Networks.
Inci Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil Jain and Jiayu Zhou
Proceedings of the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017)

REMIX: Automated Exploration for Interactive Outlier Detection
Yanjie Fu, Charu Aggarwal, Srinivasan Parthasarathy, Deepak S. Turaga, Hui Xiong
23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017

Foresight: Recommending Visual Insights [Demo and Workshop]
Cagatay Demiralp, PeterJ. Haas, Srinivasan Parthasarathy, Tejaswini Pedapati
VLDB Demo Track. This paper has also been accepted for oral presentation at KDD IDEA 2017 Workshop.

Learning task structure via sparsity grouped multitask learning
Meghana Kshirsagar, Eunho Yang, Aurelie C. Lozano
ECML-PKDD 2017

GELL: Automatic Extraction of Epidemiological Line Lists from Open Sources
Saurav Ghosh, Prithwish Chakraborty, Bryan L Lewis, Maimuna S Majumder, Emily Cohn, John S Brownstein, Madhav V Marathe, Naren Ramakrishnan
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1477--1485, 2017

Learning from multi-modality multi-resolution data: an optimization appraoch
Y. Zhu, J. Li, and J. He
SIAM International Conference on Data Mining, 2017
Abstract

HiMuV: Hierarchical Framework for Modeling Multi-Modality Multi-Resolution Data
J. Li, Y. Zhu, and J. He
IEEE-ICDM, 2017

Local Algorithm for User Action Prediction Towards Display Ads
H. Yang, Y. Zhu, and J. He
KDD, 2017

Scalable and interpretable product recommendations via overlapping co-clustering
R. Heckel, M. Vlachos, T. Parnell, C. Duenner
IEEE International Conference on Data Engineering (ICDE), 2017

Tone Analyzer for Online Customer Service: An Unsupervised Model with Interfered Training
Peifeng Yin, Zhe Liu, Anbang Xu, Taiga Nakamura
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1887--1895, ACM

2016

Regularized Weighted Linear Regression for High-dimensional Censored Data
Yan Li, Bhanukiran Vinzamuri, Chandan K. Reddy
Proceedings of the 2016 SIAM International Conference on Data Mining (SDM), pp. 45--53

Feature Grouping Using Weighted l1 Norm for High-Dimensional Data
Bhanukiran Vinzamuri, Karthik K. Padthe, Chandan K. Reddy
IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain, pp. 1233--1238

Discovering Spatial Regions of High Correlation
P. Agarwal, R. Verma, V. M. V. Gunturi
2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)

Sparse Gaussian Markov Random Field Mixtures for Anomaly Detection
Id{\'e}, Tsuyoshi and Khandelwal, Ankush and Kalagnanam, Jayant
Data Mining (ICDM), 2016 IEEE 16th International Conference on, pp. 955--960
Abstract

LSHDB: a parallel and distributed engine for record linkage and similarity search
Karapiperis, Dimitrios and Gkoulalas-Divanis, Aris and Verykios, Vassilios S
Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on, pp. 1--4
Abstract

Query-Based Evolutionary Graph Cuboid Outlier Detection
Dalmia, Ayushi and Gupta, Manish and Varma, Vasudeva
Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on, pp. 85--92

POI Recommendation: A Temporal Matching between POI Popularity and User Regularity
Zijun Yao, Yanjie Fu, Bin Liu, Yanchi Liu, Hui Xiong
2016 IEEE Conference on Data Mining (ICDM 2016)

Efficient Algorithms for the Three Locus Problem in Genome-Wide Association Study
Rajasekaran, Sanguthevar and Saha, Subrata
Data Mining (ICDM), 2016 IEEE 16th International Conference on, pp. 1155--1160

Applicability of Latent Dirichlet Allocation for Company Modeling
Katsiaryna Mirylenka, Christoph Miksovic, Paolo Scotton
In Proceedings of 16th Industrial Conference on Data Mining (ICDM), pp. 55-60, 2016

Cognito: Automated Feature Engineering for Supervised Learning
Udayan Khurana, Deepak Turaga, Horst Samulowitz, Srinivasan Parthasrathy
ICDM, 2016

Probabilistic-mismatch anomaly detection: do one's medications match with the diagnoses?
Lingxiao Zhang, Xiang Li, Haifeng Liu, Jing Mei, Gang Hu, Junfeng Zhao, Bing Xie, Guotong Xie
IEEE International Conference on Data Mining (ICDM), 2016

RelSim: Relation similarity search in schema-rich heterogeneous information networks
Wang, Chenguang and Sun, Yizhou and Song, Yanglei and Han, Jiawei and Song, Yangqiu and Wang, Lidan and Zhang, Ming
Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 621--629
Abstract

Process Trace Clustering: A Heterogeneous Information Network Approach
Phuong Nguyen, Aleksander Slominski, Vinod Muthusamy, Vatche Ishakian, Klara Nahrstedt
SIAM International Conference on Data Mining, pp. 279-287, 2016

Singapore in Motion: insights on public transport service level through farecard and mobile data analytics
H. Poonawala, V. Kolar, S. Blandin, L. Wynter and S. Sahu
22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), San Francisco, CA, August, 2016

Co-Clustering Structureal Temporal Data with Applications to Semiconductor Manufacturing
Y. Zhu and J. He
ACM Transactions on Knowledge Discovery from Data (TKDD)10, 2016
Abstract

Dynamic and Robust Wildfire Risk Prediction System: An Unsupervised Approach
Salehi, Mahsa and Rusu, Laura Irina and Lynar, Timothy and Phan, Anna
22nd ACM SIGKDD Knowledge Discovery and Data Mining, 2016

Revisiting Random Binning Feature: Fast Convergence and Strong Parallelizability
Lingfei Wu*, Ian E.H. Yen*, Jie Chen, and Rui Yan (*equally contributed)
In the Proceeding of the 22th SIGKDD conference on Knowledge Discovery and Data Mining, 2016

Recurrent Neural Networks for Modeling Company-Product Time Series
Katsiaryna Mirylenka, Christoph Miksovic, Paolo Scotton
Proceedings of the 2nd ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data (AALTD), pp. 29-36, 2016

Unified Point-of-Interest Recommendation with Temporal Interval Assessment
Yanchi Liu, Chuanren Liu, Bin Liu, Meng Qu, Hui Xiong
Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1015--1024, 2016

Risk Prediction with Electronic Health Records: A Deep Learning Approach
Yu Cheng, Fei Wang, Ping Zhang, Jianying Hu
SIAM International Conference on Data Mining (SDM), 2016
Abstract

Predicting Disk Replacement towards Reliable Data Centers
Mirela Botezatu, Ioana Giurgiu, Jasmina Bogojeska, Dorothea Wiesmann
ACM SIGKDD Conference on Knowledge Discovery and Data Mining - KDD 2016:39-48

The SPMF Open-Source Data Mining Library Version 2
Philippe Fournier-Viger, Jerry Chun-Wei Lin, Antonio Gomariz, Ted Gueniche, Azadeh Soltani, Zhihong Deng, Hoang Thanh Lam
ECML/PKDD, Springer, 2016

Open Problem: Accurately Measuring Event Impacts on Time Series.
Lianhua Chi; Bo Han; Yun Wang
2nd SIGKDD Workshop on Mining and Learning from Time Series in KDD16, 2016

World Knowledge as Indirect Supervision for Document Clustering
Wang, Chenguang and Song, Yangqiu and Roth, Dan and Zhang, Ming and Han, Jiawei
ACM Transactions on Knowledge Discovery from Data (TKDD) 11(2), 13, ACM, 2016
Abstract

Semi-Markov Switching Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems
Igor Melnyk, Arindam Banerjee, Bryan Matthews, Nikunj Oza
KDD: Conference on Knowledge Discovery and Data Mining, 2016

INSIGHT: Dynamic Traffic Management Using Heterogeneous Urban Data
Nikolaos Panagiotou and 24 co-authors
ECML/PKDD, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016

NVMcached: An NVM-based Key-Value Cache
Xingbo Wu, Fan Ni, Li Zhang, Yandong Wang, Yufei Ren, Michel Hack, Zili Shao, Song Jiang
Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems, pp. 18:1--18:7, ACM, 2016

An Empirical Study on Hybrid Recommender System with Implicit Feedback
Lee, Sunhwan and Chandra, Anca and Jadav, Divyesh
Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 514--526, 2016
Abstract

2015

Health Insurance Market Risk Assessment: Covariate Shift and k-Anonymity
Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney
SDM, 2015

Toward Comprehensive Attribution of Healthcare Cost Changes
Dmitriy Katz-Rogozhnikov, Dennis Wei, Gigi Y. Yuen-Reed, Karthikeyan Natesan Ramamurthy, Aleksandra Mojsilovi c
IEEE ICDM Workshops, 2015

Identifying Employees for Re-Skilling using an Analytics-Based Approach
Karthikeyan Natesan Ramamurthy, Moninder Singh, Michael Davis, J. Alex Kevern, Michael Peran
IEEE ICDM Workshops, 2015

Toward comprehensive attribution of healthcare cost changes
Dmitriy A. Katz-Rogozhnikov, Dennis Wei, Gigi Y. Yuen-Reed, Karthikeyan Natesan Ramamurthy, Aleksandra Mojsilovic
IEEE International Conference on Data Mining Workshop (ICDMW) on Biological Data Mining and its Applications in Healthcare, pp. 146-155, 2015

Informative Prediction based on Ordinal Questionnaire Data
\bf Tsuyoshi Id\'e, Amit Dhurandhar
Proceedings of 2015 IEEE International Conference on Data Mining (ICDM 15), pp. 191--200

Knowsim: A document similarity measure on structured heterogeneous information networks
Wang, Chenguang and Song, Yangqiu and Li, Haoran and Zhang, Ming and Han, Jiawei
Data Mining (ICDM), 2015 IEEE International Conference on, pp. 1015--1020
Abstract

Identifying Employees for Re-Skilling using an Analytics-Based Approach
Karthikeyan N. Ramamurthy, Moninder Singh, Michael Davis, Jason A. Kevern, Uri Klein and Michael Peran
IEEE International Conference on Data Mining (ICDM) - Workshop on Data Mining for Service, 2015

Health insurance market risk assessment: Covariate shift and k-anonymity
Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney
SIAM International Conference on Data Mining (SDM), pp. 226-234, 2015

Flexible Sliding Windows for Kernel Regression Based Bus Arrival Time Prediction Algorithms
Hoang Thanh Lam and Eric Bouillet
ECML/PKDD 2015, Springer

Multi-View Incident Ticket Clustering for Optimal Ticket Dispatching
Mirela Botezatu, Jasmina Bogojeska, Ioana Giurgiu, Hagen Voelzer, Dorothea Wiesmann
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015

Multi-View Incident Ticket Clustering for Optimal Ticket Dispatching
Mirela Botezatu, Jasmina Bogojeska, Ioana Giurgiu, Hagen Volzer, Dorothea Wiesmann
ACM SIGKDD Conference on Knowledge Discovery and Data Mining - KDD 2015: 1711-1720

From Multiple Views to Single View: A Neural Network Approach
Subendhu Rongali, A. P. Sarath Chandar, Balaraman Ravindran
Proceedings of the Second ACM IKDD Conference on Data Sciences, pp. 104--109, ACM, 2015

Support Measure Data Description for group anomaly detection
Guevara, Jorge and Canu, St{'e}phane and Hirata, R
ODDx3 Workshop on Outlier Definition, Detection, and Description at the 21st ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD2015)
Abstract

Mobility Mining for Journey Planning in Rome
Michele Berlingerio, Veli Bicer, Adi Botea, Stefano Braghin, Nuno Lopes, Riccardo Guidotti, Francesca Pratesi
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part III, pp. 222--226

S&P360: Multidimensional Perspective on Companies from Online Data Sources
Michele Berlingerio, Stefano Braghin, Francesco Calabrese, Cody Dunne, Yiannis Gkoufas, Mauro Martino, Jamie C. Rasmussen, Steven I. Ross
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part III, pp. 320--324

Shedding light on the performance of solar panels: a data-driven view
S. A. Chen, A. Vishwanath, S. Sathe and S. Kalyanaraman
ACM SigKDD Explorations 17(2), 24 - 36, 2015
Abstract

Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature
Meenakshi Nagarajan, Angela D Wilkins, Benjamin J Bachman, Ilya B Novikov, Shenghua Bao, Peter J Haas, Mar{'i}a E Terr{'o}n-D{'i}az, Sumit Bhatia, Anbu K Adikesavan, Jacques J Labrie, others
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2019--2028, 2015

Opinion Marks: A Human-Based Computation Approach to Instill Structure into Unstructured Text on the Web
Bum Chul Kwon, Jaegul Choo, Sung-Hee Kim, Daniel Keim, Haesun Park, Ji Soo Yi
KDD 2015 Workshop on Interactive Data Exploration and Analytics (IDEAâ€™15), pp. 47--55

LINKAGE: An Approach for Comprehensive Risk Prediction for Care Management
Sun, Zhaonan and Wang, Fei and Hu, Jianying
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1145--1154, 2015

Temporal Phenotyping from Longitudinal Electronic Health Records: A Graph Based Framework
Liu, Chuanren and Wang, Fei and Hu, Jianying and Xiong, Hui
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 705--714, 2015

Incorporating world knowledge to document clustering via heterogeneous information networks
Wang, Chenguang and Song, Yangqiu and El-Kishky, Ahmed and Roth, Dan and Zhang, Ming and Han, Jiawei
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1215--1224, 2015
Abstract

Online topic-based social influence analysis for the wimbledon championships
Embar, Varun R and Bhattacharya, Indrajit and Pandit, Vinayaka and Vaculin, Roman
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1759--1768, 2015
Abstract

Predicting future scientific discoveries based on a networked analysis of the past literature
Nagarajan, Meenakshi and Wilkins, Angela D and Bachman, Benjamin J and Novikov, Ilya B and Bao, Shenghua and Haas, Peter J and Terr{\'o}n-D{\'\i}az, Mar{\'\i}a E and Bhatia, Sumit and Adikesavan, Anbu K and Labrie, Jacques J and others
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2019--2028, 2015
Abstract

Big Data System for Analyzing Risky Procurement Entities
Dhurandhar, Amit and Graves, Bruce and Ravi, Rajesh and Maniachari, Gopikrishanan and Ettl, Markus
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1741--1750, 2015
Abstract

On Data Publishing with Clustering Preservation
Michail Vlachos, Johannes Schneider, Vassilios G. Vassiliadis
TKDD 9(3): 23:1-23:30, 2015

Exploiting relevance feedback in knowledge graph search
Su, Yu and Yang, Shengqi and Sun, Huan and Srivatsa, Mudhakar and Kase, Sue and Vanni, Michelle and Yan, Xifeng
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135--1144, 2015
Abstract

Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature
Olivier Lichtarge Meenakshi Nagarajan
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015

Dynamic Poisson Autoregression for Influenza-Like-Illness Case Count Prediction
Zheng Wang, Prithwish Chakraborty, Sumiko R. Mekaru, John S. Brownstein, Jieping Ye, Naren Ramakrishnan
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1285--1294, ACM, 2015

Voltage correlations in smart meter data
Rajendu Mitra, Ramachandra Kota, Sambaran Bandyopadhyay, Vijay Arya, Brian Sullivan, Richard Mueller, Heather Storey, Gerard Labut
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1999--2008, 2015

Co-Clustering based Dual Prediction for Cargo Pricing Optimization
Y. Zhu, H. Yang and J. He
KDD, 2015
Abstract

Modelling trajectories for diabetes complications
Yadav, Pranjul and Pruinelli, Lisiane and Hangsleben, Andrew and Dey, Sanjoy and Hauwiller, Katherine and Westra, Bonnie L and Delaney, Connie W and Kumar, Vipin and Steinbach, Michael and Simon, Gyorgy J
Proceedings of the 4th Workshop on Data Mining for Medicine and Healthcare. 2015 SIAM International Conference on Data Mining
Abstract

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ML Symposium NYAS 2019

This symposium, the thirteenth in an ongoing series presented by the Machine Learning Discussion Group at the New York Academy of Sciences, will feature Keynote Presentations from leading scientists in both applied and theoretical Machine Learning and Spotlight Talks, a series of short, early career investigator presentations across a variety of topics at the frontier of Machine Learning.

CIKM 2018

The 27th ACM International Conference on Information and Knowledge Management takes place on October 22 - 26, 2018 at 'Lingotto', Turin, Italy. The theme for 2018 is "From Big Data and Big Information to Big Knowledge".

ECML-PKDD 2018

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases will take place in the Croke Park Conference Centre, Dublin, Ireland during the 10 – 14 September 2018.

COLT 2018

The 31st edition of the Conference on Learning Theory will take place at KTH Royal Institute of Technology, Stockholm, Sweden, July 5 - 9, 2018.

Knowledge Discovery and Data Mining

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