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- affiliation: Michigan State University, East Lansing, USA
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2020 – today
- 2024
[j18]Mahya G. Z. Hashemi, Pang-Ning Tan, Ehsan Jalilvand, Brook Wilke, Hamed Alemohammad
, Narendra N. Das:
Yield estimation from SAR data using patch-based deep learning and machine learning techniques. Comput. Electron. Agric. 226: 109340 (2024)- [c109]Barikisu Issaka, Young Anna Argyris, Pang-Ning Tan:
Conversational Agents as an Aid for Cancer Survivors' Information Search. HICSS 2024: 3788-3797 - [c108]Yue Deng, Asadullah Hill Galib, Pang-Ning Tan, Lifeng Luo:
Unraveling Block Maxima Forecasting Models with Counterfactual Explanation. KDD 2024: 562-573 - [i6]Anna Stephens, Francisco Santos, Pang-Ning Tan, Abdol-Hossein Esfahanian:
Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups. CoRR abs/2401.05478 (2024) - 2023
- [c107]Anna Stephens, Francisco Santos, Pang-Ning Tan, Abdol-Hossein Esfahanian:
Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups. ICDM (Workshops) 2023: 348-355 - [c106]Asadullah Hill Galib, Pang-Ning Tan, Lifeng Luo:
SimEXT: Self-supervised Representation Learning for Extreme Values in Time Series. ICDM 2023: 1031-1036 - [c105]Francisco Santos, Anna Stephens, Pang-Ning Tan, Abdol-Hossein Esfahanian:
Influence Propagation for Linear Threshold Model with Graph Neural Networks. ICDM (Workshops) 2023: 1141-1148 - [c104]Asadullah Hill Galib, Andrew McDonald, Pang-Ning Tan, Lifeng Luo:
Self-Recover: Forecasting Block Maxima in Time Series from Predictors with Disparate Temporal Coverage Using Self-Supervised Learning. IJCAI 2023: 3723-3731 - 2022
- [c103]Boyang Liu, Pang-Ning Tan, Jiayu Zhou:
Unsupervised Anomaly Detection by Robust Density Estimation. AAAI 2022: 4101-4108 - [c102]Tyler Wilson, Pang-Ning Tan, Lifeng Luo:
DeepGPD: A Deep Learning Approach for Modeling Geospatio-Temporal Extreme Events. AAAI 2022: 4245-4253 - [c101]Young Anna Argyris, Nan Zhang, Bidhan Bashyal, Pang-Ning Tan:
Using Deep Learning to Identify Linguistic Features that Facilitate or Inhibit the Propagation of Anti- and Pro-Vaccine Content on Social Media. ICDH 2022: 107-116 - [c100]Farzan Masrour, Francisco Santos, Pang-Ning Tan, Abdol-Hossein Esfahanian:
Fairness-Aware Graph Sampling for Network Analysis. ICDM 2022: 1107-1112 - [c99]Asadullah Hill Galib, Andrew McDonald, Tyler Wilson, Lifeng Luo, Pang-Ning Tan:
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data. IJCAI 2022: 2980-2986 - [c98]Andrew McDonald, Pang-Ning Tan, Lifeng Luo:
COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence. IJCAI 2022: 3328-3334 - [c97]Francisco Santos, Junke Ye, Farzan Masrour, Pang-Ning Tan, Abdol-Hossein Esfahanian:
FACS-GCN: Fairness-Aware Cost-Sensitive Boosting of Graph Convolutional Networks. IJCNN 2022: 1-8 - [c96]Tyler Wilson, Andrew McDonald, Asadullah Hill Galib, Pang-Ning Tan, Lifeng Luo:
Beyond Point Prediction: Capturing Zero-Inflated & Heavy-Tailed Spatiotemporal Data with Deep Extreme Mixture Models. KDD 2022: 2020-2028 - [i5]Andrew McDonald, Pang-Ning Tan, Lifeng Luo:
COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence. CoRR abs/2205.01224 (2022) - [i4]Asadullah Hill Galib, Andrew McDonald, Tyler Wilson, Lifeng Luo, Pang-Ning Tan:
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data. CoRR abs/2205.02441 (2022) - 2021
- [j17]Pouyan Hatami Bahman Beiglou, Lifeng Luo, Pang-Ning Tan, Lisi Pei:
Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators. Frontiers Big Data 4: 750536 (2021) - [j16]Jianpeng Xu, Jiayu Zhou, Pang-Ning Tan, Xi Liu, Lifeng Luo:
Spatio-Temporal Multi-Task Learning via Tensor Decomposition. IEEE Trans. Knowl. Data Eng. 33(6): 2764-2775 (2021) - [c95]Boyang Liu, Mengying Sun, Ding Wang, Pang-Ning Tan, Jiayu Zhou:
Learning Deep Neural Networks under Agnostic Corrupted Supervision. ICML 2021: 6957-6967 - [c94]Boyang Liu, Ding Wang, Kaixiang Lin, Pang-Ning Tan, Jiayu Zhou:
RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection. IJCAI 2021: 1505-1511 - [c93]Ding Wang, Pang-Ning Tan:
JOHAN: A Joint Online Hurricane Trajectory and Intensity Forecasting Framework. KDD 2021: 1677-1685 - [i3]Boyang Liu, Mengying Sun, Ding Wang, Pang-Ning Tan, Jiayu Zhou:
Learning Deep Neural Networks under Agnostic Corrupted Supervision. CoRR abs/2102.06735 (2021) - 2020
- [c92]Farzan Masrour, Tyler Wilson, Heng Yan, Pang-Ning Tan, Abdol-Hossein Esfahanian:
Bursting the Filter Bubble: Fairness-Aware Network Link Prediction. AAAI 2020: 841-848 - [c91]Ding Wang, Boyang Liu, Pang-Ning Tan, Lifeng Luo:
OMuLeT: Online Multi-Lead Time Location Prediction for Hurricane Trajectory Forecasting. AAAI 2020: 963-970 - [c90]Farzan Masrour, Pang-Ning Tan, Abdol-Hossein Esfahanian:
Fairness Perception from a Network-Centric Perspective. ICDM 2020: 1178-1183 - [c89]Tyler Wilson, Pang-Ning Tan, Lifeng Luo:
Convolutional Methods for Predictive Modeling of Geospatial Data. SDM 2020: 28-36 - [i2]Farzan Masrour, Pang-Ning Tan, Abdol-Hossein Esfahanian:
Fairness Perception from a Network-Centric Perspective. CoRR abs/2010.05887 (2020)
2010 – 2019
- 2019
- [b2]Pang-Ning Tan, Michael S. Steinbach, Anuj Karpatne, Vipin Kumar:
Introduction to Data Mining (Second Edition). Pearson 2019 - [c88]Courtland VanDam, Farzan Masrour, Pang-Ning Tan, Tyler Wilson:
You have been CAUTE!: early detection of compromised accounts on social media. ASONAM 2019: 25-32 - [c87]Farzan Masrour, Pang-Ning Tan, Abdol-Hossein Esfahanian:
OPTANE: an OPtimal transport algorithm for NEtwork alignment. ASONAM 2019: 448-451 - [c86]Xi Liu, Tyler Wilson, Pang-Ning Tan, Lifeng Luo:
Hierarchical LSTM Framework for Long-Term Sea Surface Temperature Forecasting. DSAA 2019: 41-50 - [c85]Boyang Liu, Pang-Ning Tan, Jiayu Zhou:
Augmented Multi-Task Learning by Optimal Transport. SDM 2019: 19-27 - [c84]Qi Wang, Claire Boudreau, Qixing Luo, Pang-Ning Tan, Jiayu Zhou:
Deep Multi-view Information Bottleneck. SDM 2019: 37-45 - [i1]Shuai Yuan, Pang-Ning Tan, Kendra Spence Cheruvelil, Sarah M. Collins, Patricia A. Soranno:
Spatially Constrained Spectral Clustering Algorithms for Region Delineation. CoRR abs/1905.08451 (2019) - 2018
- [j15]Jingbo Meng, Wei Peng, Pang-Ning Tan, Wuyu Liu, Ying Cheng, Arram Bae:
Diffusion size and structural virality: The effects of message and network features on spreading health information on twitter. Comput. Hum. Behav. 89: 111-120 (2018) - [c83]Courtland VanDam, Pang-Ning Tan, Jiliang Tang, Hamid Karimi:
CADET: A Multi-View Learning Framework for Compromised Account Detection on Twitter. ASONAM 2018: 471-478 - [c82]Farzan Masrour, Pang-Ning Tan, Abdol-Hossein Esfahanian, Courtland VanDam:
Attributed Network Representation Learning Approaches for Link Prediction. ASONAM 2018: 560-563 - [c81]Tyler Wilson, Pang-Ning Tan, Lifeng Luo:
A Low Rank Weighted Graph Convolutional Approach to Weather Prediction. ICDM 2018: 627-636 - [c80]Xi Liu, Pang-Ning Tan, Zubin Abraham, Lifeng Luo, Pouyan Hatami:
Distribution Preserving Multi-task Regression for Spatio-Temporal Data. ICDM 2018: 1134-1139 - [c79]Qi Wang, Pang-Ning Tan, Jiayu Zhou:
Imputing Structured Missing Values in Spatial Data with Clustered Adversarial Matrix Factorization. ICDM 2018: 1284-1289 - [c78]Jianpeng Xu, Xi Liu, Tyler Wilson, Pang-Ning Tan, Pouyan Hatami, Lifeng Luo:
MUSCAT: Multi-Scale Spatio-Temporal Learning with Application to Climate Modeling. IJCAI 2018: 2912-2918 - [c77]Boyang Liu, Pang-Ning Tan, Jiayu Zhou:
Enhancing Predictive Modeling of Nested Spatial Data through Group-Level Feature Disaggregation. KDD 2018: 1784-1793 - [c76]Xi Liu, Pang-Ning Tan, Lei Liu:
STARS: Soft Multi-Task Learning for Activity Recognition from Multi-Modal Sensor Data. PAKDD (2) 2018: 571-583 - [r6]Pang-Ning Tan:
Neural Networks. Encyclopedia of Database Systems (2nd ed.) 2018 - [r5]Pang-Ning Tan:
Receiver Operating Characteristic. Encyclopedia of Database Systems (2nd ed.) 2018 - 2017
- [j14]Jianpeng Xu, Pang-Ning Tan, Jiayu Zhou, Lifeng Luo:
Online Multi-Task Learning Framework for Ensemble Forecasting. IEEE Trans. Knowl. Data Eng. 29(6): 1268-1280 (2017) - [c75]Shuai Yuan, Jiayu Zhou, Pang-Ning Tan, C. Emi Fergus, Tyler Wagner, Patricia A. Soranno:
Multi-level Multi-task Learning for Modeling Cross-Scale Interactions in Nested Geospatial Data. ICDM 2017: 1153-1158 - [c74]Shuai Yuan, Pang-Ning Tan, Kendra Spence Cheruvelil, C. Emi Fergus, Nicholas K. Skaff, Patricia A. Soranno:
Hash-Based Feature Learning for Incomplete Continuous-Valued Data. SDM 2017: 678-686 - [c73]Xi Liu, Pang-Ning Tan, Lei Liu, Steven J. Simske:
Automated classification of EEG signals for predicting students' cognitive state during learning. WI 2017: 442-450 - [c72]Courtland VanDam, Jiliang Tang, Pang-Ning Tan:
Understanding compromised accounts on Twitter. WI 2017: 737-744 - 2016
- [c71]Jianpeng Xu, Jiayu Zhou, Pang-Ning Tan, Xi Liu, Lifeng Luo:
WISDOM: Weighted incremental spatio-temporal multi-task learning via tensor decomposition. IEEE BigData 2016: 522-531 - [c70]Ding Wang, Prakash Mandayam Comar, Pang-Ning Tan:
Crowdsourcing of network data. IJCNN 2016: 2204-2211 - [c69]Xi Liu, Han Hee Song, Mario Baldi, Pang-Ning Tan:
Macro-scale mobile app market analysis using customized hierarchical categorization. INFOCOM 2016: 1-9 - [c68]Jianpeng Xu, Kaixiang Lin, Pang-Ning Tan, Jiayu Zhou:
Synergies that Matter: Efficient Interaction Selection via Sparse Factorization Machine. SDM 2016: 108-116 - [c67]Jianpeng Xu, Pang-Ning Tan, Lifeng Luo, Jiayu Zhou:
GSpartan: a Geospatio-Temporal Multi-task Learning Framework for Multi-location Prediction. SDM 2016: 657-665 - [c66]Courtland VanDam, Pang-Ning Tan:
Detecting hashtag hijacking from Twitter. WebSci 2016: 370-371 - 2015
- [c65]Lei Liu, Pang-Ning Tan, Xi Liu:
MF-Tree: Matrix Factorization Tree for Large Multi-Class Learning. CIKM 2015: 881-890 - [c64]Shuai Yuan, Pang-Ning Tan, Kendra Spence Cheruvelil, Sarah M. Collins, Patricia A. Soranno:
Constrained spectral clustering for regionalization: Exploring the trade-off between spatial contiguity and landscape homogeneity. DSAA 2015: 1-10 - [c63]Jianpeng Xu, Jiayu Zhou, Pang-Ning Tan:
FORMULA: FactORized MUlti-task LeArning for task discovery in personalized medical models. SDM 2015: 496-504 - 2014
- [j13]Zubin Abraham, Pang-Ning Tan, Perdinan, Julie A. Winkler, Shiyuan Zhong, Malgorzata Liszewska:
Contour regression: A distribution-regularized regression framework for climate modeling. Stat. Anal. Data Min. 7(4): 272-281 (2014) - [c62]Lei Liu, Sabyasachi Saha, Ruben Torres, Jianpeng Xu, Pang-Ning Tan, Antonio Nucci, Marco Mellia:
Detecting malicious clients in ISP networks using HTTP connectivity graph and flow information. ASONAM 2014: 150-157 - [c61]Jianpeng Xu, Pang-Ning Tan, Lifeng Luo:
ORION: Online Regularized Multi-task Regression and Its Application to Ensemble Forecasting. ICDM 2014: 1061-1066 - [e3]Mohammed Javeed Zaki, Zoran Obradovic, Pang-Ning Tan, Arindam Banerjee, Chandrika Kamath, Srinivasan Parthasarathy:
Proceedings of the 2014 SIAM International Conference on Data Mining, Philadelphia, Pennsylvania, USA, April 24-26, 2014. SIAM 2014, ISBN 978-1-61197-344-0 [contents] - 2013
- [c60]Prakash Mandayam Comar, Lei Liu, Sabyasachi Saha, Pang-Ning Tan, Antonio Nucci:
Combining supervised and unsupervised learning for zero-day malware detection. INFOCOM 2013: 2022-2030 - [c59]Zubin Abraham, Pang-Ning Tan, Perdinan, Julie Winkler, Shiyuan Zhong, Malgorzata Liszewska:
Position Preserving Multi-Output Prediction. ECML/PKDD (2) 2013: 320-335 - [c58]Lei Liu, Prakash Mandayam Comar, Antonio Nucci, Sabyasachi Saha, Pang-Ning Tan:
Missing or Inapplicable: Treatment of Incomplete Continuous-valued Features in Supervised Learning. SDM 2013: 46-54 - [c57]Zubin Abraham, Malgorzata Liszewska, Perdinan, Pang-Ning Tan, Julie Winkler, Shiyuan Zhong:
Distribution Regularized Regression Framework for Climate Modeling. SDM 2013: 333-341 - [p3]Ronald Nussbaum, Abdol-Hossein Esfahanian, Pang-Ning Tan:
Clustering Social Networks Using Distance-Preserving Subgraphs. The Influence of Technology on Social Network Analysis and Mining 2013: 331-349 - 2012
- [j12]Prakash Mandayam Comar, Pang-Ning Tan, Anil K. Jain:
Simultaneous classification and community detection on heterogeneous network data. Data Min. Knowl. Discov. 25(3): 420-449 (2012) - [j11]Prakash Mandayam Comar, Pang-Ning Tan, Anil K. Jain:
A framework for joint community detection across multiple related networks. Neurocomputing 76(1): 93-104 (2012) - [c56]Prakash Mandayam Comar, Lei Liu, Sabyasachi Saha, Antonio Nucci, Pang-Ning Tan:
Weighted linear kernel with tree transformed features for malware detection. CIKM 2012: 2287-2290 - [c55]Lei Liu, Prakash Mandayam Comar, Sabyasachi Saha, Pang-Ning Tan, Antonio Nucci:
Recursive NMF: Efficient label tree learning for large multi-class problems. ICPR 2012: 2148-2151 - [e2]Pang-Ning Tan, Sanjay Chawla, Chin Kuan Ho, James Bailey:
Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29-June 1, 2012, Proceedings, Part I. Lecture Notes in Computer Science 7301, Springer 2012, ISBN 978-3-642-30216-9 [contents] - [e1]Pang-Ning Tan, Sanjay Chawla, Chin Kuan Ho, James Bailey:
Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29 - June 1, 2012, Proceedings, Part II. Lecture Notes in Computer Science 7302, Springer 2012, ISBN 978-3-642-30219-0 [contents] - 2011
- [j10]Samah Jamal Fodeh, William F. Punch, Pang-Ning Tan:
On ontology-driven document clustering using core semantic features. Knowl. Inf. Syst. 28(2): 395-421 (2011) - [c54]Zubin Abraham, Pang-Ning Tan, Fan Xin:
Smoothed Quantile Regression for Statistical Downscaling of Extreme Events in Climate Modeling. CIDU 2011: 92-106 - [c53]Prakash Mandayam Comar, Pang-Ning Tan, Anil K. Jain:
LinkBoost: A Novel Cost-Sensitive Boosting Framework for Community-Level Network Link Prediction. ICDM 2011: 131-140 - [c52]Feilong Chen, Supranamaya Ranjan, Pang-Ning Tan:
Detecting bots via incremental LS-SVM learning with dynamic feature adaptation. KDD 2011: 386-394 - [p2]Jerry Scripps, Ronald Nussbaum, Pang-Ning Tan, Abdol-Hossein Esfahanian:
Link-Based Network Mining. Structural Analysis of Complex Networks 2011: 403-419 - 2010
- [j9]Jerry Scripps, Pang-Ning Tan:
Constrained overlapping clusters: minimizing the negative effects of bridge-nodes. Stat. Anal. Data Min. 3(1): 20-37 (2010) - [j8]Haibin Cheng, Pang-Ning Tan, Rong Jin:
Efficient Algorithm for Localized Support Vector Machine. IEEE Trans. Knowl. Data Eng. 22(4): 537-549 (2010) - [c51]Ronald Nussbaum, Abdol-Hossein Esfahanian, Pang-Ning Tan:
Clustering Social Networks Using Distance-Preserving Subgraphs. ASONAM 2010: 380-385 - [c50]Prakash Mandayam Comar, Pang-Ning Tan, Anil Kumar Jain:
Multi task learning on multiple related networks. CIKM 2010: 1737-1740 - [c49]Zubin Abraham, Pang-Ning Tan:
An Integrated Framework for Simultaneous Classification and Regression of Time-Series Data. SDM 2010: 653-664 - [c48]Lei Liu, Pang-Ning Tan:
A Framework for Co-classification of Articles and Users in Wikipedia. Web Intelligence 2010: 212-215 - [c47]Prakash Mandayam Comar, Pang-Ning Tan, Anil Kumar Jain:
Identifying Cohesive Subgroups and Their Correspondences in Multiple Related Networks. Web Intelligence 2010: 476-483
2000 – 2009
- 2009
- [c46]Jerry Scripps, Pang-Ning Tan, Feilong Chen, Abdol-Hossein Esfahanian:
A Matrix Alignment Approach for Collective Classification. ASONAM 2009: 155-159 - [c45]Ronald Nussbaum, Abdol-Hossein Esfahanian, Pang-Ning Tan:
History-Based Email Prioritization. ASONAM 2009: 364-365 - [c44]Feilong Chen, Pang-Ning Tan, Anil K. Jain:
A co-classification framework for detecting web spam and spammers in social media web sites. CIKM 2009: 1807-1810 - [c43]Zubin Abraham, Pang-Ning Tan:
A Semi-supervised Framework for Simultaneous Classification and Regression of Zero-Inflated Time Series Data with Application to Precipitation Prediction. ICDM Workshops 2009: 644-649 - [c42]Jerry Scripps, Pang-Ning Tan, Abdol-Hossein Esfahanian:
Measuring the effects of preprocessing decisions and network forces in dynamic network analysis. KDD 2009: 747-756 - [c41]Samah Jamal Fodeh, William F. Punch, Pang-Ning Tan:
Combining statistics and semantics via ensemble model for document clustering. SAC 2009: 1446-1450 - [c40]Haibin Cheng, Pang-Ning Tan, Christopher Potter, Steven A. Klooster:
Detection and Characterization of Anomalies in Multivariate Time Series. SDM 2009: 413-424 - [r4]Hui Xiong, Michael S. Steinbach, Pang-Ning Tan, Vipin Kumar, Wenjun Zhou:
Pattern Preserving Clustering. Encyclopedia of Data Warehousing and Mining 2009: 1505-1510 - [r3]Pang-Ning Tan:
Neural Networks. Encyclopedia of Database Systems 2009: 1906-1909 - [r2]Pang-Ning Tan:
Receiver Operating Characteristic. Encyclopedia of Database Systems 2009: 2349-2352 - 2008
- [j7]H. D. K. Moonesinghe, Pang-Ning Tan:
Outrank: a Graph-Based Outlier Detection Framework Using Random Walk. Int. J. Artif. Intell. Tools 17(1): 19-36 (2008) - [c39]Haibin Cheng, Pang-Ning Tan, Christopher Potter, Steven A. Klooster:
Data mining for visual exploration and detection of ecosystem disturbances. GIS 2008: 60 - [c38]Haibin Cheng, Pang-Ning Tan, Christopher Potter, Steven A. Klooster:
A Robust Graph-Based Algorithm for Detection and Characterization of Anomalies in Noisy Multivariate Time Series. ICDM Workshops 2008: 349-358 - [c37]Jerry Scripps, Pang-Ning Tan, Feilong Chen, Abdol-Hossein Esfahanian:
A matrix alignment approach for link prediction. ICPR 2008: 1-4 - [c36]Haibin Cheng, Ruofei Zhang, Yefei Peng, Jianchang Mao, Pang-Ning Tan:
Maximum Margin Active Learning for Sequence Labeling with Different Length. ICDM 2008: 345-359 - [c35]Haibin Cheng, Pang-Ning Tan:
Semi-supervised learning with data calibration for long-term time series forecasting. KDD 2008: 133-141 - [c34]Feilong Chen, Jerry Scripps, Pang-Ning Tan:
Link Mining for a Social Bookmarking Web Site. Web Intelligence 2008: 169-175 - [p1]Shyam Boriah, Vipin Kumar, Michael S. Steinbach, Pang-Ning Tan, Christopher Potter, Steven A. Klooster:
Detecting Ecosystem Disturbances and Land Cover Change Using Data Mining. Next Generation of Data Mining 2008 - 2007
- [c33]Hamed Valizadegan, Pang-Ning Tan:
A Prototype-driven Framework for Change Detection in Data Stream Classification. CIDM 2007: 88-95 - [c32]Haibin Cheng, Pang-Ning Tan, Jon Sticklen, William F. Punch:
Recommendation via Query Centered Random Walk on K-Partite Graph. ICDM 2007: 457-462 - [c31]Jerry Scripps, Pang-Ning Tan, Abdol-Hossein Esfahanian:
Exploration of Link Structure and Community-Based Node Roles in Network Analysis. ICDM 2007: 649-654 - [c30]Samah Jamal Fodeh, Pang-Ning Tan:
Incorporating Background Knowledge for Subjective Rule Evaluation. ICTAI (2) 2007: 148-155 - [c29]H. D. K. Moonesinghe, Hamed Valizadegan, Samah Jamal Fodeh, Pang-Ning Tan:
A Probabilistic Substructure-Based Approach for Graph Classification. ICTAI (1) 2007: 346-349 - [c28]Hamed Valizadegan, Pang-Ning Tan:
Kernel Based Detection of Mislabeled Training Examples. SDM 2007: 309-319 - [c27]Haibin Cheng, Pang-Ning Tan, Rong Jin:
Localized Support Vector Machine and Its Efficient Algorithm. SDM 2007: 461-466 - 2006
- [j6]Hui Xiong, Pang-Ning Tan, Vipin Kumar:
Hyperclique pattern discovery. Data Min. Knowl. Discov. 13(2): 219-242 (2006) - [j5]Bo Wang, Sohraab Soltani, Jonathan K. Shapiro, Pang-Ning Tan:
Local Detection of Selfish Routing Behavior in Ad Hoc Networks. J. Interconnect. Networks 7(1): 133-146 (2006) - [j4]Hui Xiong, Shashi Shekhar, Pang-Ning Tan, Vipin Kumar:
TAPER: A Two-Step Approach for All-Strong-Pairs Correlation Query in Large Databases. IEEE Trans. Knowl. Data Eng. 18(4): 493-508 (2006) - [c26]Jing Gao, Pang-Ning Tan:
Converting Output Scores from Outlier Detection Algorithms into Probability Estimates. ICDM 2006: 212-221 - [c25]H. D. K. Moonesinghe, Samah Jamal Fodeh, Pang-Ning Tan:
Frequent Closed Itemset Mining Using Prefix Graphs with an Efficient Flow-Based Pruning Strategy. ICDM 2006: 426-435 - [c24]Brian D. Connelly, Christopher W. Bowron, Li Xiao, Pang-Ning Tan, Chen Wang:
Adaptively Routing P2P Queries Using Association Analysis. ICPP 2006: 281-288 - [c23]H. D. K. Moonesinghe, Pang-Ning Tan:
Outlier Detection Using Random Walks. ICTAI 2006: 532-539 - [c22]Haibin Cheng, Pang-Ning Tan, Jing Gao, Jerry Scripps:
Multistep-Ahead Time Series Prediction. PAKDD 2006: 765-774 - [c21]Jing Gao, Haibin Cheng, Pang-Ning Tan:
Semi-supervised outlier detection. SAC 2006: 635-636 - [c20]Jerry Scripps, Pang-Ning Tan:
Clustering in the Presence of Bridge-Nodes. SDM 2006: 270-281 - [c19]Jing Gao, Pang-Ning Tan, Haibin Cheng:
Semi-Supervised Clustering with Partial Background Information. SDM 2006: 489-493 - [c18]Jing Gao, Haibin Cheng, Pang-Ning Tan:
A Novel Framework for Incorporating Labeled Examples into Anomaly Detection. SDM 2006: 594-598 - 2005
- [b1]Pang-Ning Tan, Michael S. Steinbach, Vipin Kumar:
Introduction to Data Mining. Addison-Wesley 2005, ISBN 0-321-32136-7 - [c17]Bo Wang, Sohraab Soltani, Jonathan K. Shapiro, Pang-Ning Tan:
Local Detection of Selfish Routing Behavior in Ad Hoc Networks. ISPAN 2005: 392-399 - [c16]Jing Gao, Jianzhong Li, Zhaogong Zhang, Pang-Ning Tan:
An Incremental Data Stream Clustering Algorithm Based on Dense Units Detection. PAKDD 2005: 420-425 - 2004
- [j3]Pang-Ning Tan, Vipin Kumar, Jaideep Srivastava:
Selecting the right objective measure for association analysis. Inf. Syst. 29(4): 293-313 (2004) - [c15]Behrouz Minaei-Bidgoli, Pang-Ning Tan, William F. Punch:
Mining interesting contrast rules for a web-based educational system. ICMLA 2004: 320-327 - [c14]Michael S. Steinbach, Pang-Ning Tan, Vipin Kumar:
Support envelopes: a technique for exploring the structure of association patterns. KDD 2004: 296-305 - [c13]Hui Xiong, Shashi Shekhar, Pang-Ning Tan, Vipin Kumar:
Exploiting a support-based upper bound of Pearson's correlation coefficient for efficiently identifying strongly correlated pairs. KDD 2004: 334-343 - [c12]Michael S. Steinbach, Pang-Ning Tan, Hui Xiong, Vipin Kumar:
Generalizing the notion of support. KDD 2004: 689-694 - [c11]Pang-Ning Tan, Rong Jin:
Ordering patterns by combining opinions from multiple sources. KDD 2004: 695-700 - [c10]Aysel Ozgur, Pang-Ning Tan, Vipin Kumar:
RBA: An Integrated Framework for Regression based on Association Rules. SDM 2004: 210-221 - [c9]Hui Xiong, Michael S. Steinbach, Pang-Ning Tan, Vipin Kumar:
HICAP: Hierarchical Clustering with Pattern Preservation. SDM 2004: 279-290 - [r1]Vipin Kumar, Pang-Ning Tan, Michael S. Steinbach:
Data Mining. Handbook of Data Structures and Applications 2004 - 2003
- [c8]Hui Xiong, Pang-Ning Tan, Vipin Kumar:
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution. ICDM 2003: 387-394 - [c7]Michael S. Steinbach, Pang-Ning Tan, Vipin Kumar, Steven A. Klooster, Christopher Potter:
Discovery of climate indices using clustering. KDD 2003: 446-455 - 2002
- [j2]Pang-Ning Tan, Vipin Kumar:
Discovery of Web Robot Sessions Based on their Navigational Patterns. Data Min. Knowl. Discov. 6(1): 9-35 (2002) - [c6]Pang-Ning Tan, Vipin Kumar, Jaideep Srivastava:
Selecting the right interestingness measure for association patterns. KDD 2002: 32-41 - [c5]Vipin Kumar, Mahesh V. Joshi, Eui-Hong Han, Pang-Ning Tan, Michael S. Steinbach:
High Performance Data Mining. VECPAR 2002: 111-125 - 2001
- [c4]Pang-Ning Tan, Vipin Kumar:
Mining Indirect Associations in Web Data. WEBKDD 2001: 145-166 - 2000
- [j1]Jaideep Srivastava, Robert Cooley, Mukund Deshpande, Pang-Ning Tan:
Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explor. 1(2): 12-23 (2000) - [c3]Pang-Ning Tan, Hannah Blau, Steven A. Harp, Robert P. Goldman:
Textual data mining of service center call records. KDD 2000: 417-423 - [c2]Pang-Ning Tan, Vipin Kumar, Jaideep Srivastava:
Indirect Association: Mining Higher Order Dependencies in Data. PKDD 2000: 632-637
1990 – 1999
- 1999
- [c1]Robert Cooley, Pang-Ning Tan, Jaideep Srivastava:
Discovery of Interesting Usage Patterns from Web Data. WEBKDD 1999: 163-182
Coauthor Index
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