Rankcluster is the first R package dedicated to ranking data. This package proposes modelling and clustering tools for ranking data, potentially multivariate and partial. Ranking data are modelled by the Insertion Sorting Rank (isr) model, which is a meaningful model parametrized by a central ranking and a dispersion parameter. A conditional independence assumption allows to take into account m...

A novel methodology is proposed for clustering multivariate time series data using energy distance defined in Székely and Rizzo (2013). Specifically, a dissimilarity matrix formed the statistic to measure separation between finite-dimensional distributions component series. Once pairwise calculated, hierarchical method then applied obtain dendrogram. This procedure completely nonparametric as d...

Rankcluster is the first R package proposing both modelling and clustering tools for ranking data, potentially multivariate and partial. Ranking data are modelled by the Insertion Sorting Rank (isr) model, which is a meaningful model parametrized by a central ranking and a dispersion parameter. A conditional independence assumption allows to take into account multivariate rankings, and clusteri...

This project attempts to predict the interest spread of a property loan based on the borrower and property related attributes. Each attribute can be regarded as a potential feature. The problem is how to predict the spread accurately based on those features. This report describes our approaches of using linear and segmented linear models as well as other clustering methods. The comparative resu...

A new methodology for clustering multivariate time-series data is proposed. The methodology is based on calculation of the degree of similarity between multivariate time-series datasets using two similarity factors. One similarity factor is based on principal component analysis and the angles between the principal component subspaces while the other is based on the Mahalanobis distance between ...