We propose a new filter function for Topological Data Analysis (TDA) based on a new data-dependent kernel.
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.
We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead.
We propose a multi-dimensional scaling method, named DScale, which rescales based on the computed distance.
We propose a new statistical model which contains a latent real-valued matrix for capturing the relatedness of response categories alongside variables for worker expertise, item difficulty and item true labels.
A generic data dependent dissimilarity, named massbased dissimilarity, is proposed to allow for different implementations.