We propose a new filter function for Topological Data Analysis (TDA) based on a new data-dependent kernel.
We propose the first clustering algorithm that employs an adaptive distributional kernel without any optimization, while achieving a similar optimization objective function.
We propose a distributional treatment for anomalous subsequence detection with a linear runtime.
We propose a novel efficient hierarchical clustering called StreaKHC that enables massive streaming data to be mined. .
We presents a new insight into improving the performance of Stochastic Neighbour Embedding (t-SNE) by using Isolation kernel instead of Gaussian kernel.
Replacing Gaussian kernel with Isolation kernel in t-SNE significantly improves the quality of the final visualisation output.
Investigating the data-dependent similarity measures for distance-based learning algorithms. The source code of the latest data-dependent similarity measure **aNNE** (AAAI-19) can be obtained from **[here](https://github.com/zhuye88/anne-dbscan-demo)**.
We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead.
We propose to use mass-based dissimilarity, which employs estimates of the probability mass to measure dissimilarity, to replace the distance metric.
A generic data dependent dissimilarity, named massbased dissimilarity, is proposed to allow for different implementations.