An M-of-N Rule Induction Algorithm and its Application to DNA Domain. - Kai Ming Ting (HICSS (5), 1994)

Towards using a Single Uniform Metric in Instance-Based Learning. - Kai Ming Ting (ICCBR, 1995)

The Characterisation of Predictive Accuracy and Decision Combination. - Kai Ming Ting (ICML, 1996)

Model Combination in the Multiple-Data-Batches Scenario. - Kai Ming Ting; Boon Toh Low (ECML, 1997)

Stacking Bagged and Dagged Models. - Kai Ming Ting; Ian H. Witten (ICML, 1997)

Stacked Generalizations: When Does It Work? - Kai Ming Ting; Ian H. Witten (IJCAI (2), 1997)

Discretisation in Lazy Learning Algorithms. - Kai Ming Ting (Artif. Intell. Rev., 1997)

Decision Combination Based on the Characterisation of Predictive Accuracy. - Kai Ming Ting (Intell. Data Anal., 1997)

Boosting Cost-Sensitive Trees. - Kai Ming Ting; Zijian Zheng 002 (Discovery Science, 1998)

Boosting Trees for Cost-Sensitive Classifications. - Kai Ming Ting; Zijian Zheng 002 (ECML, 1998)

Integrating boosting and stochastic attribute selection committees for further improving the performance of decision tree learning. - Zijian Zheng 002; Geoffrey I. Webb; Kai Ming Ting (ICTAI, 1998)

Inducing Cost-Sensitive Trees via Instance Weighting. - Kai Ming Ting (PKDD, 1998)

Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees. - Zijian Zheng 002; Geoffrey I. Webb; Kai Ming Ting (ICML, 1999)

A fuzzy neural network for data mining: dealing with the problem of small disjuncts. - Yakov Frayman; Kai Ming Ting; Lipo Wang (IJCNN, 1999)

Improving the Performance of Boosting for Naive Bayesian Classification. - Kai Ming Ting; Zijian Zheng 002 (PAKDD, 1999)

Issues in Stacked Generalization. - Kai Ming Ting; Ian H. Witten (J. Artif. Intell. Res., 1999)

Learning from Batched Data: Model Combination Versus Data Combination. - Kai Ming Ting; Boon Toh Low; Ian H. Witten (Knowl. Inf. Syst., 1999)

An Empirical Study of MetaCost Using Boosting Algorithms. - Kai Ming Ting (ECML, 2000)

A Comparative Study of Cost-Sensitive Boosting Algorithms. - Kai Ming Ting (ICML, 2000)

A Study on the Effect of Class Distribution Using Cost-Sensitive Learning. - Kai Ming Ting (Discovery Science, 2002)

Issues in Classifier Evaluation using Optimal Cost Curves. - Kai Ming Ting (ICML, 2002)

An Instance-Weighting Method to Induce Cost-Sensitive Trees. - Kai Ming Ting (IEEE Trans. Knowl. Data Eng., 2002)

Model Stability: A key factor in determining whether an algorithm produces an optimal model from a matching distribution. - Kai Ming Ting; Regina Jing Ying Quek (ICDM, 2003)

A Study of AdaBoost with Naive Bayesian Classifiers: Weakness and Improvement. - Kai Ming Ting; Zijian Zheng 002 (Comput. Intell., 2003)

Improving the Centered CUSUMS Statistic for Structural Break Detection in Time Series. - Kwok Pan Pang; Kai Ming Ting (Australian Conference on Artificial Intelligence, 2004)

Matching Model Versus Single Model: A Study of the Requirement to Match Class Distribution Using Decision Trees. - Kai Ming Ting (ECML, 2004)

Ensemble Selection for SuperParent-One-Dependence Estimators. - Ying Yang 001; Kevin B. Korb; Kai Ming Ting; Geoffrey I. Webb (Australian Conference on Artificial Intelligence, 2005)

Maximizing Tree Diversity by Building Complete-Random Decision Trees. - Fei Tony Liu; Kai Ming Ting; Wei Fan 001 (PAKDD, 2005)

On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. - Geoffrey I. Webb; Kai Ming Ting (Mach. Learn., 2005)

Ehipasiko: A Content-based Image Indexing and Retrieval System. - Shyh Wei Teng; Kai Ming Ting (AMT, 2006)

z-SVM: An SVM for Improved Classification of Imbalanced Data. - Tasadduq Imam; Kai Ming Ting; Joarder Kamruzzaman (Australian Conference on Artificial Intelligence, 2006)

Reproducing the Results of Ant-based Clustering Without Using Ants. - Swee Chuan Tan; Kai Ming Ting; Shyh Wei Teng (IEEE Congress on Evolutionary Computation, 2006)

To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles. - Ying Yang 001; Geoffrey I. Webb; Jesús Cerquides; Kevin B. Korb; Janice R. Boughton; Kai Ming Ting (ECML, 2006)

Variable Randomness in Decision Tree Ensembles. - Fei Tony Liu; Kai Ming Ting (PAKDD, 2006)

Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering. - Swee Chuan Tan; Kai Ming Ting; Shyh Wei Teng (ACAL, 2007)

Cocktail Ensemble for Regression. - Yang Yu 001; Zhi-Hua Zhou; Kai Ming Ting (ICDM, 2007)

Classifying under computational resource constraints: anytime classification using probabilistic estimators. - Ying Yang 001; Geoffrey I. Webb; Kevin B. Korb; Kai Ming Ting (Mach. Learn., 2007)

To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators. - Ying Yang 001; Geoffrey I. Webb; Jesús Cerquides; Kevin B. Korb; Janice R. Boughton; Kai Ming Ting (IEEE Trans. Knowl. Data Eng., 2007)

Issues of grid-cluster retrievals in swarm-based clustering. - Swee Chuan Tan; Kai Ming Ting; Shyh Wei Teng (IEEE Congress on Evolutionary Computation, 2008)

Isolation Forest. - Fei Tony Liu; Kai Ming Ting; Zhi-Hua Zhou (ICDM, 2008)

Spectrum of Variable-Random Trees. - Fei Tony Liu; Kai Ming Ting; Yang Yu 001; Zhi-Hua Zhou (J. Artif. Intell. Res., 2008)

FaSS: Ensembles for Stable Learners. - Kai Ming Ting; Jonathan R. Wells; Swee Chuan Tan; Shyh Wei Teng; Geoffrey I. Webb (MCS, 2009)

Boosting Support Vector Machines Successfully. - Kai Ming Ting; Lian Zhu (MCS, 2009)

A Comparative Study of a Practical Stochastic Clustering Method with Traditional Methods. - Swee Chuan Tan; Kai Ming Ting; Shyh Wei Teng (Australasian Conference on Artificial Intelligence, 2010)

Multi-dimensional Mass Estimation and Mass-based Clustering. - Kai Ming Ting; Jonathan R. Wells (ICDM, 2010)

Learning Naive Bayes Classifiers for Music Classification and Retrieval. - Zhouyu Fu; Guojun Lu; Kai Ming Ting; Dengsheng Zhang (ICPR, 2010)

Mass estimation and its applications. - Kai Ming Ting; Guang-Tong Zhou; Fei Tony Liu; James Swee Chuan Tan (KDD, 2010)

On Detecting Clustered Anomalies Using SCiForest. - Fei Tony Liu; Kai Ming Ting; Zhi-Hua Zhou (ECML/PKDD (2), 2010)

On Feature Combination for Music Classification. - Zhouyu Fu; Guojun Lu; Kai Ming Ting; Dengsheng Zhang (SSPR/SPR, 2010)

Best papers from the 12th Pacific-Asia conference on knowledge discovery and data mining (PAKDD2008). - Takashi Washio; Einoshin Suzuki; Kai Ming Ting (Knowl. Inf. Syst., 2010)

Confusion Matrix. - Kai Ming Ting (Encyclopedia of Machine Learning, 2010)

Error Rate. - Kai Ming Ting (Encyclopedia of Machine Learning, 2010)

Precision. - Kai Ming Ting (Encyclopedia of Machine Learning, 2010)

Precision and Recall. - Kai Ming Ting (Encyclopedia of Machine Learning, 2010)

Sensitivity and Specificity. - Kai Ming Ting (Encyclopedia of Machine Learning, 2010)

Density Estimation Based on Mass. - Kai Ming Ting; Takashi Washio; Jonathan R. Wells; Fei Tony Liu (ICDM, 2011)

On Low-Rank Regularized Least Squares for Scalable Nonlinear Classification. - Zhouyu Fu; Guojun Lu; Kai Ming Ting; Dengsheng Zhang (ICONIP (2), 2011)

Fast Anomaly Detection for Streaming Data. - Swee Chuan Tan; Kai Ming Ting; Fei Tony Liu (IJCAI, 2011)

Building Sparse Support Vector Machines for Multi-Instance Classification. - Zhouyu Fu; Guojun Lu; Kai Ming Ting; Dengsheng Zhang (ECML/PKDD (1), 2011)

Feature-subspace aggregating: ensembles for stable and unstable learners. - Kai Ming Ting; Jonathan R. Wells; Swee Chuan Tan; Shyh Wei Teng; Geoffrey I. Webb (Mach. Learn., 2011)

A general stochastic clustering method for automatic cluster discovery. - Swee Chuan Tan; Kai Ming Ting; Shyh Wei Teng (Pattern Recognit., 2011)

Music classification via the bag-of-features approach. - Zhouyu Fu; Guojun Lu; Kai Ming Ting; Dengsheng Zhang (Pattern Recognit. Lett., 2011)

Simplifying and improving ant-based clustering. - Swee Chuan Tan; Kai Ming Ting; Shyh Wei Teng (ICCS, 2011)

A Survey of Audio-Based Music Classification and Annotation. - Zhouyu Fu; Guojun Lu; Kai Ming Ting; Dengsheng Zhang (IEEE Trans. Multim., 2011)

A non-time series approach to vehicle related time series problems. - Jonathan R. Wells; Kai Ming Ting; Naiwala P. Chandrasiri (AusDM, 2012)

Learning Sparse Kernel Classifiers in the Primal. - Zhouyu Fu; Guojun Lu; Kai Ming Ting; Dengsheng Zhang (SSPR/SPR, 2012)

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification. - Geoffrey I. Webb; Janice R. Boughton; Fei Zheng 001; Kai Ming Ting; Houssam Salem (Mach. Learn., 2012)

Relevance feature mapping for content-based multimedia information retrieval. - Guang-Tong Zhou; Kai Ming Ting; Fei Tony Liu; Yilong Yin (Pattern Recognit., 2012)

Isolation-Based Anomaly Detection. - Fei Tony Liu; Kai Ming Ting; Zhi-Hua Zhou (ACM Trans. Knowl. Discov. Data, 2012)

Optimizing Cepstral Features for Audio Classification. - Zhouyu Fu; Guojun Lu; Kai Ming Ting; Dengsheng Zhang (IJCAI, 2013)

MassBayes: A New Generative Classifier with Multi-dimensional Likelihood Estimation. - Sunil Aryal; Kai Ming Ting (PAKDD (1), 2013)

Local Models - the Key to Boosting Stable Learners Successfully. - Kai Ming Ting; Lian Zhu; Jonathan R. Wells (Comput. Intell., 2013)

DEMass: a new density estimator for big data. - Kai Ming Ting; Takashi Washio; Jonathan R. Wells; Fei Tony Liu; Sunil Aryal (Knowl. Inf. Syst., 2013)

Mass estimation. - Kai Ming Ting; Guang-Tong Zhou; Fei Tony Liu; Swee Chuan Tan (Mach. Learn., 2013)

Efficient nonlinear classification via low-rank regularised least squares. - Zhouyu Fu; Guojun Lu; Kai Ming Ting; Dengsheng Zhang (Neural Comput. Appl., 2013)

Learning Sparse Kernel Classifiers for Multi-Instance Classification. - Zhouyu Fu; Guojun Lu; Kai Ming Ting; Dengsheng Zhang (IEEE Trans. Neural Networks Learn. Syst., 2013)

Mp-Dissimilarity: A Data Dependent Dissimilarity Measure. - Sunil Aryal; Kai Ming Ting; Gholamreza Haffari; Takashi Washio (ICDM, 2014)

Efficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble. - Tharindu R. Bandaragoda; Kai Ming Ting; David W. Albrecht; Fei Tony Liu; Jonathan R. Wells (ICDM Workshops, 2014)

Improving iForest with Relative Mass. - Sunil Aryal; Kai Ming Ting; Jonathan R. Wells; Takashi Washio (PAKDD (2), 2014)

LiNearN: A new approach to nearest neighbour density estimator. - Jonathan R. Wells; Kai Ming Ting; Takashi Washio (Pattern Recognit., 2014)

Beyond tf-idf and Cosine Distance in Documents Dissimilarity Measure. - Sunil Aryal; Kai Ming Ting; Gholamreza Haffari; Takashi Washio (AIRS, 2015)

LeSiNN: Detecting Anomalies by Identifying Least Similar Nearest Neighbours. - Guansong Pang; Kai Ming Ting; David W. Albrecht (ICDM Workshops, 2015)

Half-space mass: a maximally robust and efficient data depth method. - Bo Chen 009; Kai Ming Ting; Takashi Washio; Gholamreza Haffari (Mach. Learn., 2015)

Overcoming Key Weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity Measure. - Kai Ming Ting; Ye Zhu 002; Mark James Carman; Yue Zhu 001; Zhi-Hua Zhou (KDD, 2016)

Revisiting Attribute Independence Assumption in Probabilistic Unsupervised Anomaly Detection. - Sunil Aryal; Kai Ming Ting; Gholamreza Haffari (PAISI, 2016)

A Generic Ensemble Approach to Estimate Multidimensional Likelihood in Bayesian Classifier Learning. - Sunil Aryal; Kai Ming Ting (Comput. Intell., 2016)

ZERO++: Harnessing the Power of Zero Appearances to Detect Anomalies in Large-Scale Data Sets. - Guansong Pang; Kai Ming Ting; David W. Albrecht; Huidong Jin 001 (J. Artif. Intell. Res., 2016)

Commentary: a decomposition of the outlier detection problem into a set of supervised learning problems. - Ye Zhu 002; Kai Ming Ting (Mach. Learn., 2016)

Density-ratio based clustering for discovering clusters with varying densities. - Ye Zhu 002; Kai Ming Ting; Mark James Carman (Pattern Recognit., 2016)

Discover Multiple Novel Labels in Multi-Instance Multi-Label Learning. - Yue Zhu 001; Kai Ming Ting; Zhi-Hua Zhou (AAAI, 2017)

New Class Adaptation Via Instance Generation in One-Pass Class Incremental Learning. - Yue Zhu 001; Kai Ming Ting; Zhi-Hua Zhou (ICDM, 2017)

A simple efficient density estimator that enables fast systematic search. - Jonathan R. Wells; Kai Ming Ting (CoRR, 2017)

Data-dependent dissimilarity measure: an effective alternative to geometric distance measures. - Sunil Aryal; Kai Ming Ting; Takashi Washio; Gholamreza Haffari (Knowl. Inf. Syst., 2017)

Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors. - Kai Ming Ting; Takashi Washio; Jonathan R. Wells; Sunil Aryal (Mach. Learn., 2017)

Classification Under Streaming Emerging New Classes: A Solution Using Completely-Random Trees. - Xin Mu; Kai Ming Ting; Zhi-Hua Zhou (IEEE Trans. Knowl. Data Eng., 2017)

Confusion Matrix. - Kai Ming Ting (Encyclopedia of Machine Learning and Data Mining, 2017)

Error Rate. - Kai Ming Ting (Encyclopedia of Machine Learning and Data Mining, 2017)

Precision. - Kai Ming Ting (Encyclopedia of Machine Learning and Data Mining, 2017)

Precision and Recall. - Kai Ming Ting (Encyclopedia of Machine Learning and Data Mining, 2017)

Sensitivity and Specificity. - Kai Ming Ting (Encyclopedia of Machine Learning and Data Mining, 2017)

Improving Deep Forest by Confidence Screening. - Ming Pang; Kai-Ming Ting; Peng Zhao 006; Zhi-Hua Zhou (ICDM, 2018)

Which Outlier Detector Should I use? - Kai Ming Ting; Sunil Aryal; Takashi Washio (ICDM, 2018)

Isolation Kernel and Its Effect on SVM. - Kai Ming Ting; Yue Zhu 001; Zhi-Hua Zhou (KDD, 2018)

Neighbourhood Contrast: A Better Means to Detect Clusters Than Density. - Bo Chen 009; Kai Ming Ting (PAKDD (3), 2018)

A Distance Scaling Method to Improve Density-Based Clustering. - Ye Zhu 002; Kai Ming Ting; Maia Angelova (PAKDD (3), 2018)

Isolation-based anomaly detection using nearest-neighbor ensembles. - Tharindu R. Bandaragoda; Kai Ming Ting; David W. Albrecht; Fei Tony Liu; Ye Zhu 002; Jonathan R. Wells (Comput. Intell., 2018)

CDF Transform-Shift: An effective way to deal with inhomogeneous density datasets. - Ye Zhu 002; Kai Ming Ting; Mark J. Carman; Maia Angelova (CoRR, 2018)

Local contrast as an effective means to robust clustering against varying densities. - Bo Chen 009; Kai Ming Ting; Takashi Washio; Ye Zhu 002 (Mach. Learn., 2018)

Grouping points by shared subspaces for effective subspace clustering. - Ye Zhu 002; Kai Ming Ting; Mark J. Carman (Pattern Recognit., 2018)

Data Dependent Dissimilarity Measures (NII Shonan Meeting 2018-13). - Kai Ming Ting; Takashi Washio; Ata Kabán (NII Shonan Meet. Rep., 2018)

Multi-Label Learning with Emerging New Labels. - Yue Zhu 001; Kai Ming Ting; Zhi-Hua Zhou (IEEE Trans. Knowl. Data Eng., 2018)

Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering. - Xiaoyu Qin; Kai Ming Ting; Ye Zhu 002; Vincent C. S. Lee (AAAI, 2019)

Nearest Neighbor Ensembles: An Effective Method for Difficult Problems in Streaming Classification with Emerging New Classes. - Xin-Qiang Cai; Peng Zhao 006; Kai-Ming Ting; Xin Mu; Yuan Jiang 001 (ICDM, 2019)

Isolation Set-Kernel and Its Application to Multi-Instance Learning. - Bi-Cun Xu; Kai Ming Ting; Zhi-Hua Zhou (KDD, 2019)

A new simple and effective measure for bag-of-word inter-document similarity measurement. - Sunil Aryal; Kai Ming Ting; Takashi Washio; Gholamreza Haffari (CoRR, 2019)

Improving Stochastic Neighbour Embedding fundamentally with a well-defined data-dependent kernel. - Ye Zhu 002; Kai Ming Ting (CoRR, 2019)

Lowest probability mass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms. - Kai Ming Ting; Ye Zhu 002; Mark J. Carman; Yue Zhu 001; Takashi Washio; Zhi-Hua Zhou (Mach. Learn., 2019)

A new simple and efficient density estimator that enables fast systematic search. - Jonathan R. Wells; Kai Ming Ting (Pattern Recognit. Lett., 2019)

Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection. - Kai Ming Ting; Bi-Cun Xu; Takashi Washio; Zhi-Hua Zhou (KDD, 2020)

Anomaly Detection via Neighbourhood Contrast. - Bo Chen 009; Kai Ming Ting; Tat-Jun Chin (PAKDD (2), 2020)

A New Effective and Efficient Measure for Outlying Aspect Mining. - Durgesh Samariya; Sunil Aryal; Kai Ming Ting; Jiangang Ma (WISE (2), 2020)

Clustering based on Point-Set Kernel. - Kai Ming Ting; Jonathan R. Wells; Ye Zhu 002 (CoRR, 2020)

Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection. - Kai Ming Ting; Bi-Cun Xu; Takashi Washio; Zhi-Hua Zhou (CoRR, 2020)

A comparative study of data-dependent approaches without learning in measuring similarities of data objects. - Sunil Aryal; Kai Ming Ting; Takashi Washio; Gholamreza Haffari (Data Min. Knowl. Discov., 2020)

Simple supervised dissimilarity measure: Bolstering iForest-induced similarity with class information without learning. - Jonathan R. Wells; Sunil Aryal; Kai Ming Ting (Knowl. Inf. Syst., 2020)

Isolation Graph Kernel. - Bi-Cun Xu; Kai Ming Ting; Yuan Jiang 001 (AAAI, 2021)

Isolation Kernel Density Estimation. - Kai Ming Ting; Takashi Washio; Jonathan R. Wells; Hang Zhang 003 (ICDM, 2021)

Reconstruction-based Anomaly Detection with Completely Random Forest. - Yi-Xuan Xu; Ming Pang; Ji Feng; Kai Ming Ting; Yuan Jiang 001; Zhi-Hua Zhou (SDM, 2021)

Breaking the curse of dimensionality with Isolation Kernel. - Kai Ming Ting; Takashi Washio; Ye Zhu 002; Yang Xu (CoRR, 2021)

Isolation kernel: the X factor in efficient and effective large scale online kernel learning. - Kai Ming Ting; Jonathan R. Wells; Takashi Washio (Data Min. Knowl. Discov., 2021)

Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel. - Ye Zhu 002; Kai Ming Ting (J. Artif. Intell. Res., 2021)

CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities. - Ye Zhu 002; Kai Ming Ting; Mark J. Carman; Maia Angelova (Pattern Recognit., 2021)

Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract). - Ye Zhu 002; Kai Ming Ting (IJCAI, 2022)

Streaming Hierarchical Clustering Based on Point-Set Kernel. - Xin Han; Ye Zhu 002; Kai Ming Ting; De-Chuan Zhan; Gang Li 009 (KDD, 2022)

Detecting Change Intervals with Isolation Distributional Kernel. - Yang Cao 019; Ye Zhu 002; Kai Ming Ting; Flora D. Salim; Hong Xian Li; Gang Li 009 (CoRR, 2022)

Hierarchical clustering that takes advantage of both density-peak and density-connectivity. - Ye Zhu 002; Kai Ming Ting; Yuan Jin; Maia Angelova (Inf. Syst., 2022)

A New Distributional Treatment for Time Series and An Anomaly Detection Investigation. - Kai Ming Ting; Zongyou Liu; Hang Zhang 003; Ye Zhu 002 (Proc. VLDB Endow., 2022)

Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest. - Xiangyu Song; Sunil Aryal; Kai Ming Ting; Zhen Liu 006; Bin He (IEEE Trans. Geosci. Remote. Sens., 2022)

Improving Deep Forest by Screening. - Ming Pang; Kai Ming Ting; Peng Zhao 006; Zhi-Hua Zhou (IEEE Trans. Knowl. Data Eng., 2022)

Distribution-Based Trajectory Clustering. - Zijing Wang; Ye Zhu 002; Kai Ming Ting (ICDM, 2023)

Towards a Persistence Diagram that is Robust to Noise and Varied Densities. - Hang Zhang 003; Kaifeng Zhang; Kai Ming Ting; Ye Zhu 002 (ICML, 2023)

Subgraph Centralization: A Necessary Step for Graph Anomaly Detection. - Zhong Zhuang; Kai Ming Ting; Guansong Pang; Shuaibin Song (SDM, 2023)

A principled distributional approach to trajectory similarity measurement. - Yufan Wang; Kai Ming Ting; Yuanyi Shang (CoRR, 2023)

Isolation Kernel Estimators. - Kai Ming Ting; Takashi Washio; Jonathan R. Wells; Hang Zhang 003; Ye Zhu 002 (Knowl. Inf. Syst., 2023)

Isolation Distributional Kernel: A New Tool for Point and Group Anomaly Detections. - Kai Ming Ting; Bi-Cun Xu; Takashi Washio; Zhi-Hua Zhou (IEEE Trans. Knowl. Data Eng., 2023)

Point-Set Kernel Clustering. - Kai Ming Ting; Jonathan R. Wells; Ye Zhu 002 (IEEE Trans. Knowl. Data Eng., 2023)

Kernel-based clustering via Isolation Distributional Kernel. - Ye Zhu 002; Kai Ming Ting (Inf. Syst., 2023)

The impact of isolation kernel on agglomerative hierarchical clustering algorithms. - Xin Han; Ye Zhu 002; Kai Ming Ting; Gang Li 009 (Pattern Recognit., 2023)

Detecting Change Intervalswith Isolation Distributional Kernel (Abstract Reprint). - Yang Cao 019; Ye Zhu 002; Kai Ming Ting; Flora D. Salim; Hong Xian Li; Luxing Yang; Gang Li 009 (IJCAI, 2024)

Local Subsequence-Based Distribution for Time Series Clustering. - Lei Gong; Hang Zhang 003; Zongyou Liu; Kai Ming Ting; Yang Cao 019; Ye Zhu 002 (PAKDD (1), 2024)

Distributional Kernel: An Effective and Efficient Means for Trajectory Retrieval. - Yuanyi Shang; Kai Ming Ting; Zijing Wang; Yufan Wang (PAKDD (5), 2024)

Is it possible to find the single nearest neighbor of a query in high dimensions? - Kai Ming Ting; Takashi Washio; Ye Zhu 002; Yang Xu; Kaifeng Zhang (Artif. Intell., 2024)

Anomaly Detection Based on Isolation Mechanisms: A Survey. - Yang Cao 019; Haolong Xiang; Hang Zhang 003; Ye Zhu 002; Kai Ming Ting (CoRR, 2024)

Distributed Clustering based on Distributional Kernel. - Hang Zhang 003; Yang Xu; Lei Gong; Ye Zhu 002; Kai Ming Ting (CoRR, 2024)

RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observations. - Kaichen Zhou; Yang Cao 019; Taewhan Kim; Hao Zhao 002; Hao Dong 003; Kai Ming Ting; Ye Zhu 002 (CoRR, 2024)

An Online Automatic Modulation Classification Scheme Based on Isolation Distributional Kernel. - Xinpeng Li; Zile Jiang; Kai Ming Ting; Ye Zhu (CoRR, 2024)

Detecting Change Intervals with Isolation Distributional Kernel. - Yang Cao 019; Ye Zhu 002; Kai Ming Ting; Flora D. Salim; Hong Xian Li; Luxing Yang; Gang Li 009 (J. Artif. Intell. Res., 2024)

A Principled Distributional Approach to Trajectory Similarity Measurement and its Application to Anomaly Detection. - Yufan Wang; Zijing Wang; Kai Ming Ting; Yuanyi Shang (J. Artif. Intell. Res., 2024)

A new distributional treatment for time series anomaly detection. - Kai Ming Ting; Zongyou Liu; Lei Gong; Hang Zhang 003; Ye Zhu 002 (VLDB J., 2024)

What Are Anomalies in a Network? - Kai Ming Ting; Zhong Zhuang; Guansong Pang; Zongyou Liu; Tianrun Liang; Qiuran Zhao (ACM Trans. Knowl. Discov. Data, 2025)

Revisiting streaming anomaly detection: benchmark and evaluation. - Yang Cao 019; Yixiao Ma; Ye Zhu 002; Kai Ming Ting (Artif. Intell. Rev., 2025)

Voronoi Diagram Encoded Hashing. - Yang Xu; Kai Ming Ting (CoRR, 2025)

Contrastive Multi-View Graph Hashing. - Yang Xu; Zuliang Yang; Kai Ming Ting (CoRR, 2025)