Chapter 3 Similarity Measures Data Mining Technology 2. Clustering (HAC) •Assumes a similarity function for determining the similarity of two clusters. They include: 1. vectors of gene expression data), and q is a positive integer q q p p q q j x i x j INTRODUCTION: For algorithms like the k-nearest neighbor and k-means, it is essential to measure the distance between the data points.. Clustering Distance Measures Hierarchical Clustering k-Means Algorithms. Introduction 1.1. For example, consider the following data. The Manhattan distance (also called taxicab norm or 1-norm) is given by: 3.The maximum norm is given by: 4. Introduction to Clustering Techniques. •Basic algorithm: Common Distance Measures Distance measure will determine how the similarity of two elements is calculated and it will influence the shape of the clusters. In KNN we calculate the distance between points to find the nearest neighbor, and in K-Means we find the distance between points to group data points into clusters based on similarity. Chapter 3 Similarity Measures Written by Kevin E. Heinrich Presented by Zhao Xinyou [email_address] 2007.6.7 Some materials (Examples) are taken from Website. Introduction to Hierarchical Clustering Analysis Dinh Dong Luong Introduction Data clustering concerns how to group a set of objects based on their similarity of ... – A free PowerPoint PPT presentation (displayed as a Flash slide show) on PowerShow.com - id: 71f70a-MTNhM The requirements for a function on pairs of points to be a distance measure are that: a space is just a universal set of points, from which the points in the dataset are drawn. 4 1. •Starts with all instances in a separate cluster and then repeatedly joins the two clusters that are most similar until there is only one cluster. Similarity Measures for Binary Data Similarity measures between objects that contain only binary attributes are called similarity coefficients, and typically have values between 0 and 1. The Euclidean distance (also called 2-norm distance) is given by: 2. If meaningful clusters are the goal, then the resulting clusters should capture the “natural” 10 Example : Protein Sequences Objects are sequences of {C,A,T,G}. Here, the contribution of Cost 2 and Cost 3 is insignificant compared to Cost 1 so far the Euclidean distance … Clustering is a useful technique that organizes a large quantity of unordered text documents into a small number of meaningful and coherent cluster. A value of 1 indicates that the two objects are completely similar, while a value of 0 indicates that the objects are not at all similar. A major problem when using the similarity (or dissimilarity) measures (such as Euclidean distance) is that the large values frequently swamp the small ones. 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