{"id":337,"date":"2020-03-09T15:21:00","date_gmt":"2020-03-09T15:21:00","guid":{"rendered":"http:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/eleanor-darcy\/?p=337"},"modified":"2021-02-05T09:20:51","modified_gmt":"2021-02-05T09:20:51","slug":"stor-i-masterclass-professor-brendan-murphy","status":"publish","type":"post","link":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/eleanor-darcy\/2020\/03\/09\/stor-i-masterclass-professor-brendan-murphy\/","title":{"rendered":"STOR-i Masterclass: Professor Brendan Murphy"},"content":{"rendered":"\t\t
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Model-Based Clustering and Classification<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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A few weeks ago,\u00a0Professor Brendan Murphy<\/a>\u00a0visited 好色先生TV to present a two-day masterclass to all STOR-i students on Model-Based Clustering and Classification. Brendan is Full Professor and Head of School in School of Mathematics and Statistics at University College Dublin. His research interests include clustering, classification and latent variable modelling, particularly Brendan is interested in applications from social sciences, food science, medicine and biology. Currently, he is the editor for Social Sciences and Government for the Annals of Applied Statistics and he has recently co-authored a research monograph on Model-Based Clustering and Classification<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Intro<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Brendan kick-started the masterclass by providing an introduction to clustering. Cluster analysis aims to find meaningful groups in data in order to find clusters whose members have something in common that they do not share with members of other groups.\u00a0<\/span>Clustering dates back to the beginning of language – at least – when objects were grouped according to common characteristics. For example, Aristotle classified animals into groups based on observations, in ‘History of Animals’<\/em> from the 4th century BC.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Hierarchical Clustering<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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In the 1950s, various hierarchical clustering methods were introduced. These aim to build a tree of clusters so that you start with n<\/em> observations divided into n<\/em> clusters (every observation is its own, individual cluster), then you find the two ‘closest’<\/em> clusters and group them so that there are now n-1<\/em> clusters, then you continue in this way until everyone is in a cluster. In order to do this, you need a measure of distance between observations (dissimilarity) and a measure of distance between clusters (linkage). The choice of these measures can heavily influence the results. Hierarchical clustering doesn’t always perform well even though it is commonly used.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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K-means Clustering<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Another method of clustering was developed in the late 1950s: k-means clustering. Here, we describe clusters by the average of the observations within it. This is an iterative algorithm repeated until convergence, split into two steps:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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