Cluster Analysis.

Slides:

Advertisements

Similar presentations

Different types of data e.g. Continuous data:height Categorical data ordered (nominal):growth rate very slow, slow, medium, fast, very fast not ordered:fruit.

Advertisements

N. Kumar, Asst. Professor of Marketing Database Marketing Cluster Analysis.

Clustering Clustering of data is a method by which large sets of data is grouped into clusters of smaller sets of similar data. The example below demonstrates.

Non-linear Dimensionality Reduction CMPUT 466/551 Nilanjan Ray Prepared on materials from the book Non-linear dimensionality reduction By Lee and Verleysen,

Statistics for Marketing & Consumer Research Copyright © Mario Mazzocchi 1 Cluster Analysis (from Chapter 12)

6-1 ©2006 Raj Jain Clustering Techniques  Goal: Partition into groups so the members of a group are as similar as possible and different.

By Fernando Seoane, April 25 th, 2006 Demo for Non-Parametric Classification Euclidean Metric Classifier with Data Clustering.

Learning Bit by Bit Clustering. Supervised vs. Unsupervised Training vs. Exploring.

Dimension reduction : PCA and Clustering Slides by Agnieszka Juncker and Chris Workman.

Cluster Analysis.

1 Chapter 8: Clustering. 2 Searching for groups Clustering is unsupervised or undirected. Unlike classification, in clustering, no pre- classified data.

Clustering. 2 Outline  Introduction  K-means clustering  Hierarchical clustering: COBWEB.

1 Partitioning Algorithms: Basic Concepts  Partition n objects into k clusters Optimize the chosen partitioning criterion Example: minimize the Squared.

1 Kunstmatige Intelligentie / RuG KI2 - 7 Clustering Algorithms Johan Everts.

Cluster Analysis.

CLUSTERING (Segmentation)

Lecture 09 Clustering-based Learning

What is Cluster Analysis?

Data Mining Strategies. Scales of Measurement  Stevens, S.S. (1946). On the theory of scales of measurement. Science, 103,  Four Scales  Categorical.

ROUGH SET THEORY AND FUZZY LOGIC BASED WAREHOUSING OF HETEROGENEOUS CLINICAL DATABASES Yiwen Fan.

Introduction to undirected Data Mining: Clustering

Clustering Algorithms Mu-Yu Lu. What is Clustering? Clustering can be considered the most important unsupervised learning problem; so, as every other.

Cluster Analysis Part I

DATA MINING CLUSTERING K-Means.

Automated Communities in Social Networks Using Kohonen SOM By Dinesh Gadge Parthasarathi Roy.

More on Microarrays Chitta Baral Arizona State University.

1 Lecture 10 Clustering. 2 Preview Introduction Partitioning methods Hierarchical methods Model-based methods Density-based methods.

Cluster Analysis Market Segmentation Document Similarity.

1 Motivation Web query is usually two or three words long. –Prone to ambiguity –Example “keyboard” –Input device of computer –Musical instruments How can.

CLUSTERING. Overview Definition of Clustering Existing clustering methods Clustering examples.

Multidimensional Scaling Vuokko Vuori Based on: Data Exploration Using Self-Organizing Maps, Samuel Kaski, Ph.D. Thesis, 1997 Multivariate Statistical.

1 Cluster Analysis Objectives ADDRESS HETEROGENEITY Combine observations into groups or clusters such that groups formed are homogeneous (similar) within.

Computer Graphics and Image Processing (CIS-601).

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Clustering COMP Research Seminar BCB 713 Module Spring 2011 Wei Wang.

Dimension Reduction in Workers Compensation CAS predictive Modeling Seminar Louise Francis, FCAS, MAAA Francis Analytics and Actuarial Data Mining, Inc.

Data Science and Big Data Analytics Chap 4: Advanced Analytical Theory and Methods: Clustering Charles Tappert Seidenberg School of CSIS, Pace University.

K-Means Algorithm Each cluster is represented by the mean value of the objects in the cluster Input: set of objects (n), no of clusters (k) Output:

DATA MINING WITH CLUSTERING AND CLASSIFICATION Spring 2007, SJSU Benjamin Lam.

Data Mining Techniques Clustering. Purpose In clustering analysis, there is no pre-classified data Instead, clustering analysis is a process where a set.

1 Efficient and Effective Clustering Methods for Spatial Data Mining Raymond T. Ng, Jiawei Han Pavan Podila COSC 6341, Fall ‘04.

Computational Biology Clustering Parts taken from Introduction to Data Mining by Tan, Steinbach, Kumar Lecture Slides Week 9.

Fuzzy C-Means Clustering

Data Mining Algorithms

CS685 : Special Topics in Data Mining, UKY The UNIVERSITY of KENTUCKY Clustering Analysis CS 685: Special Topics in Data Mining Jinze Liu.

1 CSC 594 Topics in AI – Text Mining and Analytics Fall 2015/16 8. Text Clustering.

Clustering, performance evaluation, and Term Project 1.Term Project 2.Resource for review.

Cluster Analysis Dr. Bernard Chen Assistant Professor Department of Computer Science University of Central Arkansas.

Mr. Idrissa Y. H. Assistant Lecturer, Geography & Environment Department of Social Sciences School of Natural & Social Sciences State University of Zanzibar.

Cluster Analysis Dr. Bernard Chen Ph.D. Assistant Professor Department of Computer Science University of Central Arkansas Fall 2010.

Clustering Wei Wang. Outline What is clustering Partitioning methods Hierarchical methods Density-based methods Grid-based methods Model-based clustering.

Clustering Algorithms Minimize distance But to Centers of Groups.

1 Similarity and Dissimilarity Between Objects Distances are normally used to measure the similarity or dissimilarity between two data objects Some popular.

Clustering / Scaling. Cluster Analysis Objective: – Partitions observations into meaningful groups with individuals in a group being more “similar” to.

Cluster Analysis What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods.

Christoph F. Eick Questions Review October 12, How does post decision tree post-pruning work? What is the purpose of applying post-pruning in decision.

Topic 4: Cluster Analysis Analysis of Customer Behavior and Service Modeling.

CLARANS: A Method for Clustering Objects for Spatial Data Mining IEEE Transactions on Knowledge and Data Enginerring, 2002 Raymond T. Ng et al. 22 MAR.

1 C.A.L. Bailer-Jones. Machine Learning. Unsupervised learning and clustering Machine learning, pattern recognition and statistical data modelling Lecture.

Data Mining: Concepts and Techniques (3rd ed.) — Chapter 10 —

Topic 3: Cluster Analysis

Fuzzy Clustering.

Text Categorization Berlin Chen 2003 Reference:

Topic 5: Cluster Analysis

Clustering The process of grouping samples so that the samples are similar within each group.

Presentation transcript:

Cluster Analysis

Purpose clustering analysis is a process where a set of objects is partitioned into several clusters All members in one cluster are similar to each other and different from the members of other clusters, according to some similarity metric

Cluster Analysis Y (Age) X (Income) Cluster Customer (Object) Variables

Dissimilarity (Distance) Measure

Dissimilarity (Distance) Measure

Dissimilarity (Distance) Measure

Categorization of Clustering Methods Exclusive vs. Non-Exclusive (Overlapping) Hierarchical Methods vs. Partitioning Methods Hierarchical Methods Single Link Method Complete Link Method Partitioning Methods Kohonen Self-Organizing Maps (SOM) K-Means Methods K-Medoids Methods (PAM, CLARA, CLARANS) Demographic Methods …

Hierarchical Methods Dissimilarity Matrix (55)