Kai Ming Ting

ORCID

Publication Activity (10 Years)

Years Active: 1994-2024
Publications (10 Years): 73

Top Venues

CoRR

ICDM

IEEE Trans. Knowl. Data Eng.

KDD

Publications

Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Luxing Yang, Gang Li
Detecting Change Intervals with Isolation Distributional Kernel. J. Artif. Intell. Res. 79 (2024)
Yuanyi Shang, Kai Ming Ting, Zijing Wang, Yufan Wang
Distributional Kernel: An Effective and Efficient Means for Trajectory Retrieval. PAKDD (5) (2024)
Yufan Wang, Zijing Wang, Kai Ming Ting, Yuanyi Shang
A Principled Distributional Approach to Trajectory Similarity Measurement and its Application to Anomaly Detection. J. Artif. Intell. Res. 79 (2024)
Kai Ming Ting, Zongyou Liu, Lei Gong, Hang Zhang, Ye Zhu
A new distributional treatment for time series anomaly detection. VLDB J. 33 (3) (2024)
Yang Cao, Haolong Xiang, Hang Zhang, Ye Zhu, Kai Ming Ting
Anomaly Detection Based on Isolation Mechanisms: A Survey. CoRR (2024)
Lei Gong, Hang Zhang, Zongyou Liu, Kai Ming Ting, Yang Cao, Ye Zhu
Local Subsequence-Based Distribution for Time Series Clustering. PAKDD (1) (2024)
Zi Jing Wang, Ye Zhu, Kai Ming Ting
Distribution-Based Trajectory Clustering. CoRR (2023)
Zhong Zhuang, Kai Ming Ting, Guansong Pang, Shuaibin Song
Subgraph Centralization: A Necessary Step for Graph Anomaly Detection. SDM (2023)
Xin Han, Ye Zhu, Kai Ming Ting, Gang Li
The impact of isolation kernel on agglomerative hierarchical clustering algorithms. Pattern Recognit. 139 (2023)
Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Hang Zhang, Ye Zhu
Isolation Kernel Estimators. Knowl. Inf. Syst. 65 (2) (2023)
Kai Ming Ting, Jonathan R. Wells, Ye Zhu
Point-Set Kernel Clustering. IEEE Trans. Knowl. Data Eng. 35 (5) (2023)
Zi Jing Wang, Ye Zhu, Kai Ming Ting
Distribution-Based Trajectory Clustering. ICDM (2023)
Yufan Wang, Kai Ming Ting, Yuanyi Shang
A principled distributional approach to trajectory similarity measurement. CoRR (2023)
Zhong Zhuang, Kai Ming Ting, Guansong Pang, Shuaibin Song
Subgraph Centralization: A Necessary Step for Graph Anomaly Detection. CoRR (2023)
Kai Ming Ting, Bi-Cun Xu, Takashi Washio, Zhi-Hua Zhou
Isolation Distributional Kernel: A New Tool for Point and Group Anomaly Detections. IEEE Trans. Knowl. Data Eng. 35 (3) (2023)
Ye Zhu, Kai Ming Ting
Kernel-based clustering via Isolation Distributional Kernel. Inf. Syst. 117 (2023)
Hang Zhang, Kaifeng Zhang, Kai Ming Ting, Ye Zhu
Towards a Persistence Diagram that is Robust to Noise and Varied Densities. ICML (2023)
Ye Zhu, Kai Ming Ting, Yuan Jin, Maia Angelova
Hierarchical clustering that takes advantage of both density-peak and density-connectivity. Inf. Syst. 103 (2022)
Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Gang Li
Detecting Change Intervals with Isolation Distributional Kernel. CoRR (2022)
Ye Zhu, Kai Ming Ting
Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract). IJCAI (2022)
Xin Han, Ye Zhu, Kai Ming Ting, De-Chuan Zhan, Gang Li
Streaming Hierarchical Clustering Based on Point-Set Kernel. KDD (2022)
Ming Pang, Kai Ming Ting, Peng Zhao, Zhi-Hua Zhou
Improving Deep Forest by Screening. IEEE Trans. Knowl. Data Eng. 34 (9) (2022)
Kai Ming Ting, Zongyou Liu, Hang Zhang, Ye Zhu
A New Distributional Treatment for Time Series and An Anomaly Detection Investigation. Proc. VLDB Endow. 15 (11) (2022)
Xiangyu Song, Sunil Aryal, Kai Ming Ting, Zhen Liu, Bin He
Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest. IEEE Trans. Geosci. Remote. Sens. 60 (2022)
Kai Ming Ting, Takashi Washio, Ye Zhu, Yang Xu
Breaking the curse of dimensionality with Isolation Kernel. CoRR (2021)
Bi-Cun Xu, Kai Ming Ting, Yuan Jiang
Isolation Graph Kernel. AAAI (2021)
Yi-Xuan Xu, Ming Pang, Ji Feng, Kai Ming Ting, Yuan Jiang, Zhi-Hua Zhou
Reconstruction-based Anomaly Detection with Completely Random Forest. SDM (2021)
Ye Zhu, Kai Ming Ting, Mark J. Carman, Maia Angelova
CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities. Pattern Recognit. 117 (2021)
Kai Ming Ting, Jonathan R. Wells, Takashi Washio
Isolation kernel: the X factor in efficient and effective large scale online kernel learning. Data Min. Knowl. Discov. 35 (6) (2021)
Ye Zhu, Kai Ming Ting
Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel. J. Artif. Intell. Res. 71 (2021)
Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Hang Zhang
Isolation Kernel Density Estimation. ICDM (2021)
Jonathan R. Wells, Sunil Aryal, Kai Ming Ting
Simple supervised dissimilarity measure: Bolstering iForest-induced similarity with class information without learning. Knowl. Inf. Syst. 62 (8) (2020)
Durgesh Samariya, Sunil Aryal, Kai Ming Ting
A new effective and efficient measure for outlying aspect mining. CoRR (2020)
Xin Han, Ye Zhu, Kai Ming Ting, Gang Li
The Impact of Isolation Kernel on Agglomerative Hierarchical Clustering Algorithms. CoRR (2020)
Kai Ming Ting, Bi-Cun Xu, Takashi Washio, Zhi-Hua Zhou
Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection. KDD (2020)
Kai Ming Ting, Bi-Cun Xu, Takashi Washio, Zhi-Hua Zhou
Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection. CoRR (2020)
Bo Chen, Kai Ming Ting, Tat-Jun Chin
Anomaly Detection via Neighbourhood Contrast. PAKDD (2) (2020)
Sunil Aryal, Kai Ming Ting, Takashi Washio, Gholamreza Haffari
A comparative study of data-dependent approaches without learning in measuring similarities of data objects. Data Min. Knowl. Discov. 34 (1) (2020)
Durgesh Samariya, Sunil Aryal, Kai Ming Ting, Jiangang Ma
A New Effective and Efficient Measure for Outlying Aspect Mining. WISE (2) (2020)
Kai Ming Ting, Jonathan R. Wells, Ye Zhu
Clustering based on Point-Set Kernel. CoRR (2020)
Kai Ming Ting, Ye Zhu, Mark J. Carman, Yue Zhu, Takashi Washio, Zhi-Hua Zhou
Lowest probability mass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms. Mach. Learn. 108 (2) (2019)
Ye Zhu, Kai Ming Ting
Improving Stochastic Neighbour Embedding fundamentally with a well-defined data-dependent kernel. CoRR (2019)
Xiaoyu Qin, Kai Ming Ting, Ye Zhu, Vincent C. S. Lee
Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering. AAAI (2019)
Kai Ming Ting, Jonathan R. Wells, Takashi Washio
Isolation Kernel: The X Factor in Efficient and Effective Large Scale Online Kernel Learning. CoRR (2019)
Jonathan R. Wells, Kai Ming Ting
A new simple and efficient density estimator that enables fast systematic search. Pattern Recognit. Lett. 122 (2019)
Sunil Aryal, Kai Ming Ting, Takashi Washio, Gholamreza Haffari
A new simple and effective measure for bag-of-word inter-document similarity measurement. CoRR (2019)
Bi-Cun Xu, Kai Ming Ting, Zhi-Hua Zhou
Isolation Set-Kernel and Its Application to Multi-Instance Learning. KDD (2019)
Xiaoyu Qin, Kai Ming Ting, Ye Zhu, Vincent C. S. Lee
Nearest-Neighbour-Induced Isolation Similarity and its Impact on Density-Based Clustering. CoRR (2019)
Bo Chen, Kai Ming Ting
Neighbourhood Contrast: A Better Means to Detect Clusters Than Density. PAKDD (3) (2018)
Ye Zhu, Kai Ming Ting, Maia Angelova
A Distance Scaling Method to Improve Density-Based Clustering. PAKDD (3) (2018)
Kai Ming Ting, Sunil Aryal, Takashi Washio
Which Outlier Detector Should I use? ICDM (2018)
Ye Zhu, Kai Ming Ting, Mark J. Carman, Maia Angelova
CDF Transform-Shift: An effective way to deal with inhomogeneous density datasets. CoRR (2018)
Kai Ming Ting, Yue Zhu, Zhi-Hua Zhou
Isolation Kernel and Its Effect on SVM. KDD (2018)
Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou
Multi-Label Learning with Emerging New Labels. IEEE Trans. Knowl. Data Eng. 30 (10) (2018)
Ye Zhu, Kai Ming Ting, Mark J. Carman
Grouping points by shared subspaces for effective subspace clustering. Pattern Recognit. 83 (2018)
Kai Ming Ting, Takashi Washio, Ata Kabán
Data Dependent Dissimilarity Measures (NII Shonan Meeting 2018-13). NII Shonan Meet. Rep. 2018 (2018)
Ye Zhu, Kai Ming Ting, Yuan Jin, Maia Angelova
Hierarchical clustering that takes advantage of both density-peak and density-connectivity. CoRR (2018)
Tharindu R. Bandaragoda, Kai Ming Ting, David W. Albrecht, Fei Tony Liu, Ye Zhu, Jonathan R. Wells
Isolation-based anomaly detection using nearest-neighbor ensembles. Comput. Intell. 34 (4) (2018)
Bo Chen, Kai Ming Ting, Takashi Washio, Ye Zhu
Local contrast as an effective means to robust clustering against varying densities. Mach. Learn. 107 (8-10) (2018)
Sunil Aryal, Kai Ming Ting, Takashi Washio, Gholamreza Haffari
Data-dependent dissimilarity measure: an effective alternative to geometric distance measures. Knowl. Inf. Syst. 53 (2) (2017)
Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou
New Class Adaptation Via Instance Generation in One-Pass Class Incremental Learning. ICDM (2017)
Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou
Discover Multiple Novel Labels in Multi-Instance Multi-Label Learning. AAAI (2017)
Xin Mu, Kai Ming Ting, Zhi-Hua Zhou
Classification Under Streaming Emerging New Classes: A Solution Using Completely-Random Trees. IEEE Trans. Knowl. Data Eng. 29 (8) (2017)
Jonathan R. Wells, Kai Ming Ting
A simple efficient density estimator that enables fast systematic search. CoRR (2017)
Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Sunil Aryal
Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors. Mach. Learn. 106 (1) (2017)
Ye Zhu, Kai Ming Ting, Mark James Carman
Density-ratio based clustering for discovering clusters with varying densities. Pattern Recognit. 60 (2016)
Sunil Aryal, Kai Ming Ting
A Generic Ensemble Approach to Estimate Multidimensional Likelihood in Bayesian Classifier Learning. Comput. Intell. 32 (3) (2016)
Sunil Aryal, Kai Ming Ting, Gholamreza Haffari
Revisiting Attribute Independence Assumption in Probabilistic Unsupervised Anomaly Detection. PAISI (2016)
Ye Zhu, Kai Ming Ting
Commentary: a decomposition of the outlier detection problem into a set of supervised learning problems. Mach. Learn. 105 (2) (2016)
Guansong Pang, Kai Ming Ting, David W. Albrecht, Huidong Jin
ZERO++: Harnessing the Power of Zero Appearances to Detect Anomalies in Large-Scale Data Sets. J. Artif. Intell. Res. 57 (2016)
Xin Mu, Kai Ming Ting, Zhi-Hua Zhou
Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees. CoRR (2016)
Kai Ming Ting, Ye Zhu, Mark James Carman, Yue Zhu, Zhi-Hua Zhou
Overcoming Key Weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity Measure. KDD (2016)
Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou
Multi-label Learning with Emerging New Labels. ICDM (2016)
Guansong Pang, Kai Ming Ting, David W. Albrecht
LeSiNN: Detecting Anomalies by Identifying Least Similar Nearest Neighbours. ICDM Workshops (2015)
Bo Chen, Kai Ming Ting, Takashi Washio, Gholamreza Haffari
Half-space mass: a maximally robust and efficient data depth method. Mach. Learn. 100 (2-3) (2015)
Sunil Aryal, Kai Ming Ting, Gholamreza Haffari, Takashi Washio
Beyond tf-idf and Cosine Distance in Documents Dissimilarity Measure. AIRS (2015)
Sunil Aryal, Kai Ming Ting, Jonathan R. Wells, Takashi Washio
Improving iForest with Relative Mass. PAKDD (2) (2014)
Sunil Aryal, Kai Ming Ting, Gholamreza Haffari, Takashi Washio
Mp-Dissimilarity: A Data Dependent Dissimilarity Measure. ICDM (2014)
Tharindu R. Bandaragoda, Kai Ming Ting, David W. Albrecht, Fei Tony Liu, Jonathan R. Wells
Efficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble. ICDM Workshops (2014)
Jonathan R. Wells, Kai Ming Ting, Takashi Washio
LiNearN: A new approach to nearest neighbour density estimator. Pattern Recognit. 47 (8) (2014)
Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Fei Tony Liu, Sunil Aryal
DEMass: a new density estimator for big data. Knowl. Inf. Syst. 35 (3) (2013)
Sunil Aryal, Kai Ming Ting
MassBayes: A New Generative Classifier with Multi-dimensional Likelihood Estimation. PAKDD (1) (2013)
Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
Learning Sparse Kernel Classifiers for Multi-Instance Classification. IEEE Trans. Neural Networks Learn. Syst. 24 (9) (2013)
Kai Ming Ting, Lian Zhu, Jonathan R. Wells
Local Models - the Key to Boosting Stable Learners Successfully. Comput. Intell. 29 (2) (2013)
Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
Optimizing Cepstral Features for Audio Classification. IJCAI (2013)
Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
Efficient nonlinear classification via low-rank regularised least squares. Neural Comput. Appl. 22 (7-8) (2013)
Kai Ming Ting, Guang-Tong Zhou, Fei Tony Liu, Swee Chuan Tan
Mass estimation. Mach. Learn. 90 (1) (2013)
Guang-Tong Zhou, Kai Ming Ting, Fei Tony Liu, Yilong Yin
Relevance feature mapping for content-based multimedia information retrieval. Pattern Recognit. 45 (4) (2012)
Jonathan R. Wells, Kai Ming Ting, Naiwala P. Chandrasiri
A non-time series approach to vehicle related time series problems. AusDM (2012)
Geoffrey I. Webb, Janice R. Boughton, Fei Zheng, Kai Ming Ting, Houssam Salem
Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification. Mach. Learn. 86 (2) (2012)
Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
Learning Sparse Kernel Classifiers in the Primal. SSPR/SPR (2012)
Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou
Isolation-Based Anomaly Detection. ACM Trans. Knowl. Discov. Data 6 (1) (2012)
Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
Music classification via the bag-of-features approach. Pattern Recognit. Lett. 32 (14) (2011)
Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
On Low-Rank Regularized Least Squares for Scalable Nonlinear Classification. ICONIP (2) (2011)
Swee Chuan Tan, Kai Ming Ting, Fei Tony Liu
Fast Anomaly Detection for Streaming Data. IJCAI (2011)
Kai Ming Ting, Jonathan R. Wells, Swee Chuan Tan, Shyh Wei Teng, Geoffrey I. Webb
Feature-subspace aggregating: ensembles for stable and unstable learners. Mach. Learn. 82 (3) (2011)
Kai Ming Ting, Ian H. Witten
Issues in Stacked Generalization CoRR (2011)
Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Fei Tony Liu
Density Estimation Based on Mass. ICDM (2011)
Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
Building Sparse Support Vector Machines for Multi-Instance Classification. ECML/PKDD (1) (2011)
Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
A Survey of Audio-Based Music Classification and Annotation. IEEE Trans. Multim. 13 (2) (2011)

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Publication Activity (10 Years)

Top Topics

Top Venues

Kai Ming Ting