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# similarity and distance measures in clustering ppt

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