Towards a Persistence Diagram that is Robust to Noise and Varied Densities

We propose a new filter function for Topological Data Analysis (TDA) based on a new data-dependent kernel.

A new distributional treatment for time series and an anomaly detection investigation

We propose a distributional treatment for anomalous subsequence detection with a linear runtime.

Streaming Hierarchical Clustering Based on Point-Set Kernel

We propose a novel efficient hierarchical clustering called StreaKHC that enables massive streaming data to be mined. .

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

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

Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering

We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead.

A Distance Scaling Method to Improve Density-Based Clustering

We propose a multi-dimensional scaling method, named DScale, which rescales based on the computed distance.

Leveraging label category relationships in multi-class crowdsourcing

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.

Overcoming key weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity Measure

A generic data dependent dissimilarity, named massbased dissimilarity, is proposed to allow for different implementations.