With Government bodies, policy makers, and institutions being entrusted with directing increasingly large shares of the collective public effort (as measured by GDP) there is an enormous public interest in ensuring that the right policies are implemented at the right time, and for an appropriate duration. This has led to...
Hierarchical clustering groups data rows into trees of clusters, there are two main approaches, bottom up, and top down. Aggolmerative hierarchical clustering: every row is assigned to its own cluster initially. Clusters that are closest to each other are then merged and the process iterates with fewer/larger clusters until all...
Given a dataset of n-rows select k-rows such that k≤n. These are the seed rows for creating k clusters within the dataset. The efficacy of the method is determined by how well chosen these initial cells are. Should they be too close together, say two rows within a cluster that...
Less precise than other forms of modelling (specifically classification). The development of useful information from the data and the identification of a particular cluster often requires significant domain expertise. It is a form of unstructured learning as the algorithm defines the clusters, and groups the instances accordingly. The aim of...
There are two primary goals when applying an algorithm; we approach the data with a goal of describing the data as they are, or using the data to extrapolate about future likelihoods. Often we can combine the too too, but these can be exclusive applications. There are several different classes...