similarity and distance measures in clustering ppt

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