Search Results for author: Kai Ming Ting

Found 19 papers, 9 papers with code

Distribution-Based Trajectory Clustering

no code implementations8 Oct 2023 Zi Jing Wang, Ye Zhu, Kai Ming Ting

Independent of the distance measure employed, existing clustering algorithms have another challenge: either effectiveness issues or high time complexity.

Clustering Trajectory Clustering

Subgraph Centralization: A Necessary Step for Graph Anomaly Detection

1 code implementation17 Jan 2023 Zhong Zhuang, Kai Ming Ting, Guansong Pang, Shuaibin Song

A treatment called Subgraph Centralization for graph anomaly detection is proposed to address all the above weaknesses.

Graph Anomaly Detection

A principled distributional approach to trajectory similarity measurement

no code implementations1 Jan 2023 Yufan Wang, Kai Ming Ting, Yuanyi Shang

Existing measures and representations for trajectories have two longstanding fundamental shortcomings, i. e., they are computationally expensive and they can not guarantee the `uniqueness' property of a distance function: dist(X, Y) = 0 if and only if X=Y, where $X$ and $Y$ are two trajectories.

Anomaly Detection

Detecting Change Intervals with Isolation Distributional Kernel

2 code implementations30 Dec 2022 Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Luxing Yang, Gang Li

Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis.

Change Point Detection

The Impact of Isolation Kernel on Agglomerative Hierarchical Clustering Algorithms

no code implementations12 Oct 2020 Xin Han, Ye Zhu, Kai Ming Ting, Gang Li

In this paper, we identify the root cause of this issue and show that the use of a data-dependent kernel (instead of distance or existing kernel) provides an effective means to address it.


Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection

1 code implementation24 Sep 2020 Kai Ming Ting, Bi-Cun Xu, Takashi Washio, Zhi-Hua Zhou

Existing approaches based on kernel mean embedding, which convert a point kernel to a distributional kernel, have two key issues: the point kernel employed has a feature map with intractable dimensionality; and it is {\em data independent}.

Group Anomaly Detection

A new effective and efficient measure for outlying aspect mining

no code implementations28 Apr 2020 Durgesh Samariya, Sunil Aryal, Kai Ming Ting

In this paper, we introduce a new score called SiNNE, which is independent of the dimensionality of subspaces.

Density Estimation

Point-Set Kernel Clustering

1 code implementation14 Feb 2020 Kai Ming Ting, Jonathan R. Wells, Ye Zhu

This paper introduces a new similarity measure called point-set kernel which computes the similarity between an object and a set of objects.

Clustering Semantic Segmentation

Isolation Kernel: The X Factor in Efficient and Effective Large Scale Online Kernel Learning

no code implementations2 Jul 2019 Kai Ming Ting, Jonathan R. Wells, Takashi Washio

A current key approach focuses on ways to produce an approximate finite-dimensional feature map, assuming that the kernel used has a feature map with intractable dimensionality---an assumption traditionally held in kernel-based methods.

Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel

1 code implementation24 Jun 2019 Ye Zhu, Kai Ming Ting

This paper presents a new insight into improving the performance of Stochastic Neighbour Embedding (t-SNE) by using Isolation kernel instead of Gaussian kernel.

A new simple and effective measure for bag-of-word inter-document similarity measurement

no code implementations9 Feb 2019 Sunil Aryal, Kai Ming Ting, Takashi Washio, Gholamreza Haffari

To measure the similarity of two documents in the bag-of-words (BoW) vector representation, different term weighting schemes are used to improve the performance of cosine similarity---the most widely used inter-document similarity measure in text mining.

Hierarchical clustering that takes advantage of both density-peak and density-connectivity

1 code implementation8 Oct 2018 Ye Zhu, Kai Ming Ting, Yuan Jin, Maia Angelova

This paper focuses on density-based clustering, particularly the Density Peak (DP) algorithm and the one based on density-connectivity DBSCAN; and proposes a new method which takes advantage of the individual strengths of these two methods to yield a density-based hierarchical clustering algorithm.


CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities

1 code implementation5 Oct 2018 Ye Zhu, Kai Ming Ting, Mark Carman, Maia Angelova

To match the implicit assumption, we propose to transform a given dataset such that the transformed clusters have approximately the same density while all regions of locally low density become globally low density -- homogenising cluster density while preserving the cluster structure of the dataset.

Anomaly Detection Clustering +1

A simple efficient density estimator that enables fast systematic search

no code implementations3 Jul 2017 Jonathan R. Wells, Kai Ming Ting

We show that a recent outlying aspects miner can run orders of magnitude faster by simply replacing its density estimator with the proposed density estimator, enabling it to deal with large datasets with thousands of dimensions that would otherwise be impossible.

Small Data Image Classification

Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees

no code implementations30 May 2016 Xin Mu, Kai Ming Ting, Zhi-Hua Zhou

This is the first time, as far as we know, that completely random trees are used as a single common core to solve all three sub problems: unsupervised learning, supervised learning and model update in data streams.

Classification General Classification

Isolation forest

no code implementations15 Dec 2008 Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou

Most existing model-based approaches to anomaly detection construct a profile of normal instances, then identify instances that do not conform to the normal profile as anomalies.

Anomaly Detection Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly +4

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