Clustering课件.ppt

Clustering Petter Mostad 精品文档 Clustering vs. class prediction Class prediction: A learning set of objects with known classes Goal: put new objects into existing classes Also called: Supervised learning, or classification Clustering: No learning set, no given classes Goal: discover the ”best” classes or groupings Also called: Unsupervised learning, or class discovery 精品文档 Overview General clustering theory Steps, methods, algorithms, issues... Clustering microarray data Recommendations for this kind of data Programs for clustering Some other visualization techniques 精品文档 Issues in clustering Used to explore and visualize data, with few preconceptions Many subjective choices must be made, so a clustering output tends to be subjective It is difficult to get truly statistically ”significant” conclusions Algorithms will always produce clusters, whether any exist in the data or not 精品文档 Steps in clustering Feature selection and extraction Defining and computing similarities Clustering or grouping objects Assessing, presenting, and using the result 精品文档 1. Feature selection and extraction Deciding which measurements matter for similarity Data reduction Filtering away objects Normalization of measurements 精品文档 The data matrix Every row contains the measurements for one object. Similarities are computed between all pairs of rows If measurements are of same type, one can instead cluster them! measurements objects 精品文档 2. Defining and computing similarities Similarity measures for continuous data vectors: Euclidean distance Minkowski distance (including Manhattan metric) Mahalanobis distance where S is a covariance matrix 精品文档 Centered and non-centered (absolute) Pearson correlation centered: non-centered: where Spearman rank correlation Compute the ranking of the numbers in each vector Find correlation between ranking numbers .... 精品文档 Geometrical view of clustering If measurements are coordinates, objects become points in some space If the simia