similarity and distance measures in clustering ppt

Introduction 1.1. Here, the contribution of Cost 2 and Cost 3 is insignificant compared to Cost 1 so far the Euclidean distance … Introduction to Clustering Techniques. The Euclidean distance (also called 2-norm distance) is given by: 2. INTRODUCTION: For algorithms like the k-nearest neighbor and k-means, it is essential to measure the distance between the data points.. Clustering is a useful technique that organizes a large quantity of unordered text documents into a small number of meaningful and coherent cluster. Documents with similar sets of words may be about the same topic. 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. similarity measure 1. Scope of This Paper Cluster analysis divides data into meaningful or useful groups (clusters). Clustering Distance Measures Hierarchical Clustering k-Means Algorithms. A major problem when using the similarity (or dissimilarity) measures (such as Euclidean distance) is that the large values frequently swamp the small ones. 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. The Manhattan distance (also called taxicab norm or 1-norm) is given by: 3.The maximum norm is given by: 4. •Basic algorithm: Clustering (HAC) •Assumes a similarity function for determining the similarity of two clusters. Points, Spaces, and Distances: The dataset for clustering is a collection of points, where objects belongs to some space. For example, consider the following data. a space is just a universal set of points, from which the points in the dataset are drawn. 3 5 Minkowski distances • One group of popular distance measures for interval-scaled variables are Minkowski distances where i = (xi1, xi2, …, xip) and j = (xj1, xj2, …, xjp) are two p-dimensional data objects (e.g. If meaningful clusters are the goal, then the resulting clusters should capture the “natural” They include: 1. 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 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. 10 Example : Protein Sequences Objects are sequences of {C,A,T,G}. Chapter 3 Similarity Measures Data Mining Technology 2. •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. •The history of merging forms a binary tree or hierarchy. A wide variety of distance functions and similarity measures have been used for clustering, such as squared Euclidean distance, and cosine similarity. 4 1. 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