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Found 20 papers, 9 papers with code
Anomaly Detection Based on Isolation Mechanisms: A Survey
Anomaly detection is a longstanding and active research area that has many applications in domains such as finance, security, and manufacturing.
Distribution-Based Trajectory Clustering
Independent of the distance measure employed, existing clustering algorithms have another challenge: either effectiveness issues or high time complexity.
Subgraph Centralization: A Necessary Step for Graph Anomaly Detection
A treatment called Subgraph Centralization for graph anomaly detection is proposed to address all the above weaknesses.
A principled distributional approach to trajectory similarity measurement
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.
Detecting Change Intervals with Isolation Distributional Kernel
Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis.
Breaking the curse of dimensionality with Isolation Kernel
The curse of dimensionality has been studied in different aspects.
The Impact of Isolation Kernel on Agglomerative Hierarchical Clustering Algorithms
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
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}.
A new effective and efficient measure for outlying aspect mining
In this paper, we introduce a new score called SiNNE, which is independent of the dimensionality of subspaces.
Point-Set Kernel Clustering
This paper introduces a new similarity measure called point-set kernel which computes the similarity between an object and a set of objects.
Isolation Kernel: The X Factor in Efficient and Effective Large Scale Online Kernel Learning
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.
Nearest-Neighbour-Induced Isolation Similarity and its Impact on Density-Based Clustering
A new type of clusters called mass-connected clusters is formally defined.
Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel
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
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
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
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.
Isolation Kernel and Its Effect on SVM
This paper investigates data dependent kernels that are derived directly from data.
A simple efficient density estimator that enables fast systematic search
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.
Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees
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.
Isolation forest
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
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