I put some miscellaneous stuff on this page -- things that don't really fall under other categories. For now, please find below some posters of mine.

Evolutionary Clustering Methods

Clustering is the process of grouping a set of unlabelled data objects (usually represented as a vector of measurements in a multidimensional space) into a number of clusters. The general objective of clustering is to obtain a partitioning of the data objects such that data within the same cluster are more similar to each other compared to data in different clusters. In some applications, we not only want to obtain static clustering results for one time step, but we are also interested in clustering data objects for an extended period of time. We want to make use of the clustering information from previous time steps to help produce consistent clustering results for the current time step. Ideally, we aim to produce interpretable and efficient clustering results for a set of data objects that evolve over time. Read more

Subspace Clustering With Application To Text Data

The Office for National Statistics (ONS) are experimenting with incorporating web-scraped data into the price index generating process. Clustering methods could be used to automate this process effectively and efficiently. Text data from the same category usually have a few terms in common, which can be modelled as from the same subspace. Read more