Clustering. Data Mining. Abraham Otero. Data Mining. Agenda

Size: px
Start display at page:

Download "Clustering. Data Mining. Abraham Otero. Data Mining. Agenda"

Transcription

1 Clustering 1/46 Agenda Introduction Distance K-nearest neighbors Hierarchical clustering Quick reference 2/46 1

2 Introduction It seems logical that in a new situation we should act in a similar way as in previous similar situations, if we succeeded in them. In order to taking advantage of this strategy it is necessary to define what is meant by "similar, or the equivalent mathematical concept of "distance". It will also be necessary to determine when we are going to take advantage of this similarity: In an eager mode, processing the data available before starting the process. In a lazy mode, processing the data as it arrives. 3/46 Introduction Problem formulation: 4/46 2

3 Agenda Introduction Distance K-nearest neighbors Hierarchical clustering Quick reference Open problems References 5/46 Distance Several common distances: p-norm(euclidean p=2, Minkowski p>2) Chebyshev Manhattan 6/46 3

4 Distance Be careful when applying distances: 7/46 Distance Be careful when applying distances: 8/46 4

5 Always normalize first: Distance 9/46 Distance But when normalizing beware of outliers!: 10/46 5

6 Distance Sometimes, we need to calculate the distance between a point and a set of points: 11/46 Agenda Introduction Distance K-nearest neighbors Hierarchical clustering Quick reference Open problems References 12/46 6

7 k-nearest neighbors k-nearest neighbors algorithm (k-nn) is a method for classifying objects based on closest training examples in the feature space. It is an instance-based learning lazy algorithm. An object is classified by a majority vote of its neighbors. The object that is assigned to the class is the one that is most common amongst its k nearest neighbors. 13/46 k-nearest neighbors It is one of the simplest methods of clustering. Requires an initial set of labeled points. It is critical to determine an appropriate value for K. Try several values. Circle Square 14/46 7

8 Agenda Introduction Distance K-nearest neighbors Hierarchical clustering Quick reference Open problems References 15/46 It is prototype based clustering. Each of the existing classes is represented by a prototype vector (a fictitious instance of the class) called centroid. Once the centroids have been calculated, if we need to classify a new element we simply calculate its closest centroid; this will be its class. Centroids share space in a set of regions called Voronoi regions. 16/46 8

9 Centroid calculation: 17/46 algorithm: 18/46 9

10 Sample (successful) run: 19/46 Initialization matters: Try different initial values. 20/46 10

11 The selection of K is critical: Try different K values. K=3 K=4 21/46 Limitations: Different cluster sizes 22/46 11

12 Limitations: Different density 23/46 Limitations: Non-globular shapes 24/46 12

13 One possible solution is to use many clusters. Find parts of clusters. Then you need to put them together. 25/46 What about the nominal attributes? We can define a function if a=b, and otherwise. Therefore, the distance between two classes is given by: 26/46 13

14 KMeans demo: ering/tutorial_html/appletkm.html Applet/Code/Cluster.html 27/46 Agenda Introduction Distance K-nearest neighbors Hierarchical clustering Quick reference Open problems References 28/46 14

15 (Density-Based Spatial Clustering of Applications with Noise) is a data clustering algorithm, not prototype based. It finds a number of clusters starting from the estimated density distribution of corresponding nodes. Classifies points in three categories: A point is a core point if it has more than a specified number of points (MinPts) within a radius Eps (these points are the interior of a cluster). A border point has fewer than MinPts within Eps, but is in the neighborhood of a core point. A noise point is any point that is not a core point or a border point. 29/46 Example: 30/46 15

16 Algorithm: Classify points as noise, border and core. Eliminate noise points. Perform clustering on the remaining points. 31/46 Example: 32/46 16

17 Strong points: Resistant to noise. Can handle clusters of different shapes and sizes. Weak points: Clusters with varying densities. High-dimensional data (it usually becomes too sparse). 33/46 34/46 17

18 Parameter determination. For MinPts a small number is usually employed. For two-dimensional experimental data it has been shown that 4 is the most reasonable value. Eps is more tricky, as we have seen. A possible solution: For points in a cluster, their k th nearest neighbors are at roughly the same distance. Noise points have the k th nearest neighbor at a farther distance. So, plot sorted distance of every point to its k th nearest neighbor 35/46 Parameter determination. 36/46 18

19 demo: de/cluster.html 37/46 Agenda Introduction Distance K-nearest neighbors Grid clustering Hierarchical clustering Quick reference 38/46 19

20 Hierarchical clustering Hierarchical clustering builds a hierarchy of clusters based on distance measurements. The traditional representation of this hierarchy is a tree (called a dendrogram), with individual elements on the leaves and a single cluster containing every element at the root. The tree like diagram can be interpreted as a sequences of merges or splits. Any desired number of clusters can be obtained by cutting the dendogram at the proper level. 39/46 Hierarchical clustering There are two main types of hierarchical clustering: Agglomerative (AGNES, Agglomerative NESting): Starts with the points as individual clusters. At each step, merge the closest pair of clusters until only one cluster (or k clusters) are left. Divisive (DIANA, Divisive ANAlysis Clustering): Start with one, all-inclusive cluster. At each step, split a cluster until each cluster contains a point (or there are k clusters). In both cases, once a decision is made to combine/split two clusters, it cannot be undone. There is no global minimization. 40/46 20

21 Hierarchical clustering How to define inter-cluster distance? 41/46 Hierarchical clustering Single link Can handle non ellipitical clusters. Sensitive to noise and outliers Complete link Less sensitive to noise and outliers. Tends to break large clusters. Biased to globular clusters. Group and centroid average Less sensitive to noise and outliers Biased to globular clusters 42/46 21

22 Demo: Hierarchical clustering al_html/appleth.html 43/46 Agenda Introduction Distance K-nearest neighbors Hierarchical clustering Quick reference 44/46 22

23 Quick reference Some general tips for choosing the clustering algorithm: Prototype-based and Hierarchical clustering (except single-link) tend to form globular clusters. This is good for vector quantization but not for other kinds of data. Density-based and graph-based (except those in the previous rule) tend to form non-globular clusters. Most clustering algorithms work well for low dimensional spaces. If the dimensionality of the data is very large, think of reducing the dimensionality beforehand (PCA). 45/46 Quick reference If a taxonomy is to be created, consider hierarchical clustering. If a summarization of the data is needed, consider a partitional clustering. Can we allow the algorithm to discard outliers? (Ex: ). They might represent unusually profitable customers. Is it necessary to classify all the data? (Ex: we have to classify all documents in the database). Computing the mean makes sense only for real-value attributes (K-Means). Define an appropriate distance (Ex: Euclidean distance is valid for real-valued attributes only). 46/46 23

DATA MINING CLUSTER ANALYSIS: BASIC CONCEPTS

DATA MINING CLUSTER ANALYSIS: BASIC CONCEPTS DATA MINING CLUSTER ANALYSIS: BASIC CONCEPTS 1 AND ALGORITHMS Chiara Renso KDD-LAB ISTI- CNR, Pisa, Italy WHAT IS CLUSTER ANALYSIS? Finding groups of objects such that the objects in a group will be similar

More information

Cluster Analysis: Advanced Concepts

Cluster Analysis: Advanced Concepts Cluster Analysis: Advanced Concepts and dalgorithms Dr. Hui Xiong Rutgers University Introduction to Data Mining 08/06/2006 1 Introduction to Data Mining 08/06/2006 1 Outline Prototype-based Fuzzy c-means

More information

Data Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining

Data Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining Data Mining Cluster Analsis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining b Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining /8/ What is Cluster

More information

Data Mining Clustering (2) Sheets are based on the those provided by Tan, Steinbach, and Kumar. Introduction to Data Mining

Data Mining Clustering (2) Sheets are based on the those provided by Tan, Steinbach, and Kumar. Introduction to Data Mining Data Mining Clustering (2) Toon Calders Sheets are based on the those provided by Tan, Steinbach, and Kumar. Introduction to Data Mining Outline Partitional Clustering Distance-based K-means, K-medoids,

More information

Data Mining Cluster Analysis: Basic Concepts and Algorithms. Clustering Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining

Data Mining Cluster Analysis: Basic Concepts and Algorithms. Clustering Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining Data Mining Cluster Analsis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining b Tan, Steinbach, Kumar Clustering Algorithms K-means and its variants Hierarchical clustering

More information

Data Mining Cluster Analysis: Advanced Concepts and Algorithms. Lecture Notes for Chapter 9. Introduction to Data Mining

Data Mining Cluster Analysis: Advanced Concepts and Algorithms. Lecture Notes for Chapter 9. Introduction to Data Mining Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004

More information

Data Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining

Data Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 4/8/2004 Hierarchical

More information

Clustering. Adrian Groza. Department of Computer Science Technical University of Cluj-Napoca

Clustering. Adrian Groza. Department of Computer Science Technical University of Cluj-Napoca Clustering Adrian Groza Department of Computer Science Technical University of Cluj-Napoca Outline 1 Cluster Analysis What is Datamining? Cluster Analysis 2 K-means 3 Hierarchical Clustering What is Datamining?

More information

Data Mining. Cluster Analysis: Advanced Concepts and Algorithms

Data Mining. Cluster Analysis: Advanced Concepts and Algorithms Data Mining Cluster Analysis: Advanced Concepts and Algorithms Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 More Clustering Methods Prototype-based clustering Density-based clustering Graph-based

More information

Data Mining Cluster Analysis: Advanced Concepts and Algorithms. Lecture Notes for Chapter 9. Introduction to Data Mining

Data Mining Cluster Analysis: Advanced Concepts and Algorithms. Lecture Notes for Chapter 9. Introduction to Data Mining Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004

More information

Chapter 7. Cluster Analysis

Chapter 7. Cluster Analysis Chapter 7. Cluster Analysis. What is Cluster Analysis?. A Categorization of Major Clustering Methods. Partitioning Methods. Hierarchical Methods 5. Density-Based Methods 6. Grid-Based Methods 7. Model-Based

More information

CLASSIFICATION AND CLUSTERING. Anveshi Charuvaka

CLASSIFICATION AND CLUSTERING. Anveshi Charuvaka CLASSIFICATION AND CLUSTERING Anveshi Charuvaka Learning from Data Classification Regression Clustering Anomaly Detection Contrast Set Mining Classification: Definition Given a collection of records (training

More information

Cluster Analysis. Alison Merikangas Data Analysis Seminar 18 November 2009

Cluster Analysis. Alison Merikangas Data Analysis Seminar 18 November 2009 Cluster Analysis Alison Merikangas Data Analysis Seminar 18 November 2009 Overview What is cluster analysis? Types of cluster Distance functions Clustering methods Agglomerative K-means Density-based Interpretation

More information

Data Mining Clustering. Sheets are based on the those provided by Tan, Steinbach, and Kumar. Introduction to Data Mining

Data Mining Clustering. Sheets are based on the those provided by Tan, Steinbach, and Kumar. Introduction to Data Mining Data Mining Clustering Toon Calders Sheets are based on the those provided b Tan, Steinbach, and Kumar. Introduction to Data Mining What is Cluster Analsis? Finding groups of objects such that the objects

More information

Example: Document Clustering. Clustering: Definition. Notion of a Cluster can be Ambiguous. Types of Clusterings. Hierarchical Clustering

Example: Document Clustering. Clustering: Definition. Notion of a Cluster can be Ambiguous. Types of Clusterings. Hierarchical Clustering Overview Prognostic Models and Data Mining in Medicine, part I Cluster Analsis What is Cluster Analsis? K-Means Clustering Hierarchical Clustering Cluster Validit Eample: Microarra data analsis 6 Summar

More information

Data Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining

Data Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 by Tan, Steinbach, Kumar 1 What is Cluster Analysis? Finding groups of objects such that the objects in a group will

More information

Data Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining

Data Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining Data Mining Cluster Analsis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining b Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 4/8/4 What is

More information

Cluster Analysis: Basic Concepts and Algorithms

Cluster Analysis: Basic Concepts and Algorithms 8 Cluster Analysis: Basic Concepts and Algorithms Cluster analysis divides data into groups (clusters) that are meaningful, useful, or both. If meaningful groups are the goal, then the clusters should

More information

Clustering and Data Mining in R

Clustering and Data Mining in R Clustering and Data Mining in R Workshop Supplement Thomas Girke December 10, 2011 Introduction Data Preprocessing Data Transformations Distance Methods Cluster Linkage Hierarchical Clustering Approaches

More information

K-Means Cluster Analysis. Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1

K-Means Cluster Analysis. Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 K-Means Cluster Analsis Chapter 3 PPDM Class Tan,Steinbach, Kumar Introduction to Data Mining 4/18/4 1 What is Cluster Analsis? Finding groups of objects such that the objects in a group will be similar

More information

Cluster Analysis: Basic Concepts and Algorithms

Cluster Analysis: Basic Concepts and Algorithms Cluster Analsis: Basic Concepts and Algorithms What does it mean clustering? Applications Tpes of clustering K-means Intuition Algorithm Choosing initial centroids Bisecting K-means Post-processing Strengths

More information

Clustering UE 141 Spring 2013

Clustering UE 141 Spring 2013 Clustering UE 141 Spring 013 Jing Gao SUNY Buffalo 1 Definition of Clustering Finding groups of obects such that the obects in a group will be similar (or related) to one another and different from (or

More information

Classifying Large Data Sets Using SVMs with Hierarchical Clusters. Presented by :Limou Wang

Classifying Large Data Sets Using SVMs with Hierarchical Clusters. Presented by :Limou Wang Classifying Large Data Sets Using SVMs with Hierarchical Clusters Presented by :Limou Wang Overview SVM Overview Motivation Hierarchical micro-clustering algorithm Clustering-Based SVM (CB-SVM) Experimental

More information

. Learn the number of classes and the structure of each class using similarity between unlabeled training patterns

. Learn the number of classes and the structure of each class using similarity between unlabeled training patterns Outline Part 1: of data clustering Non-Supervised Learning and Clustering : Problem formulation cluster analysis : Taxonomies of Clustering Techniques : Data types and Proximity Measures : Difficulties

More information

ARTIFICIAL INTELLIGENCE (CSCU9YE) LECTURE 6: MACHINE LEARNING 2: UNSUPERVISED LEARNING (CLUSTERING)

ARTIFICIAL INTELLIGENCE (CSCU9YE) LECTURE 6: MACHINE LEARNING 2: UNSUPERVISED LEARNING (CLUSTERING) ARTIFICIAL INTELLIGENCE (CSCU9YE) LECTURE 6: MACHINE LEARNING 2: UNSUPERVISED LEARNING (CLUSTERING) Gabriela Ochoa http://www.cs.stir.ac.uk/~goc/ OUTLINE Preliminaries Classification and Clustering Applications

More information

ROBERTO BATTITI, MAURO BRUNATO. The LION Way: Machine Learning plus Intelligent Optimization. LIONlab, University of Trento, Italy, Apr 2015

ROBERTO BATTITI, MAURO BRUNATO. The LION Way: Machine Learning plus Intelligent Optimization. LIONlab, University of Trento, Italy, Apr 2015 ROBERTO BATTITI, MAURO BRUNATO. The LION Way: Machine Learning plus Intelligent Optimization. LIONlab, University of Trento, Italy, Apr 2015 http://intelligentoptimization.org/lionbook Roberto Battiti

More information

Cluster Analysis: Basic Concepts and Methods

Cluster Analysis: Basic Concepts and Methods 10 Cluster Analysis: Basic Concepts and Methods Imagine that you are the Director of Customer Relationships at AllElectronics, and you have five managers working for you. You would like to organize all

More information

Cluster Analysis using R

Cluster Analysis using R Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other

More information

Unsupervised learning: Clustering

Unsupervised learning: Clustering Unsupervised learning: Clustering Salissou Moutari Centre for Statistical Science and Operational Research CenSSOR 17 th September 2013 Unsupervised learning: Clustering 1/52 Outline 1 Introduction What

More information

Fig. 1 A typical Knowledge Discovery process [2]

Fig. 1 A typical Knowledge Discovery process [2] Volume 4, Issue 7, July 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Review on Clustering

More information

Cluster Analysis Overview. Data Mining Techniques: Cluster Analysis. What is Cluster Analysis? What is Cluster Analysis?

Cluster Analysis Overview. Data Mining Techniques: Cluster Analysis. What is Cluster Analysis? What is Cluster Analysis? Cluster Analsis Overview Data Mining Techniques: Cluster Analsis Mirek Riedewald Man slides based on presentations b Han/Kamber, Tan/Steinbach/Kumar, and Andrew Moore Introduction Foundations: Measuring

More information

An Introduction to Cluster Analysis for Data Mining

An Introduction to Cluster Analysis for Data Mining An Introduction to Cluster Analysis for Data Mining 10/02/2000 11:42 AM 1. INTRODUCTION... 4 1.1. Scope of This Paper... 4 1.2. What Cluster Analysis Is... 4 1.3. What Cluster Analysis Is Not... 5 2. OVERVIEW...

More information

Neural Networks Lesson 5 - Cluster Analysis

Neural Networks Lesson 5 - Cluster Analysis Neural Networks Lesson 5 - Cluster Analysis Prof. Michele Scarpiniti INFOCOM Dpt. - Sapienza University of Rome http://ispac.ing.uniroma1.it/scarpiniti/index.htm michele.scarpiniti@uniroma1.it Rome, 29

More information

Clustering. Chapter 7. 7.1 Introduction to Clustering Techniques. 7.1.1 Points, Spaces, and Distances

Clustering. Chapter 7. 7.1 Introduction to Clustering Techniques. 7.1.1 Points, Spaces, and Distances 240 Chapter 7 Clustering Clustering is the process of examining a collection of points, and grouping the points into clusters according to some distance measure. The goal is that points in the same cluster

More information

Distances, Clustering, and Classification. Heatmaps

Distances, Clustering, and Classification. Heatmaps Distances, Clustering, and Classification Heatmaps 1 Distance Clustering organizes things that are close into groups What does it mean for two genes to be close? What does it mean for two samples to be

More information

Information Retrieval and Web Search Engines

Information Retrieval and Web Search Engines Information Retrieval and Web Search Engines Lecture 7: Document Clustering December 10 th, 2013 Wolf-Tilo Balke and Kinda El Maarry Institut für Informationssysteme Technische Universität Braunschweig

More information

Clustering methods for Big data analysis

Clustering methods for Big data analysis Clustering methods for Big data analysis Keshav Sanse, Meena Sharma Abstract Today s age is the age of data. Nowadays the data is being produced at a tremendous rate. In order to make use of this large-scale

More information

A Comparative Study of clustering algorithms Using weka tools

A Comparative Study of clustering algorithms Using weka tools A Comparative Study of clustering algorithms Using weka tools Bharat Chaudhari 1, Manan Parikh 2 1,2 MECSE, KITRC KALOL ABSTRACT Data clustering is a process of putting similar data into groups. A clustering

More information

Reference Books. Data Mining. Supervised vs. Unsupervised Learning. Classification: Definition. Classification k-nearest neighbors

Reference Books. Data Mining. Supervised vs. Unsupervised Learning. Classification: Definition. Classification k-nearest neighbors Classification k-nearest neighbors Data Mining Dr. Engin YILDIZTEPE Reference Books Han, J., Kamber, M., Pei, J., (2011). Data Mining: Concepts and Techniques. Third edition. San Francisco: Morgan Kaufmann

More information

An Analysis on Density Based Clustering of Multi Dimensional Spatial Data

An Analysis on Density Based Clustering of Multi Dimensional Spatial Data An Analysis on Density Based Clustering of Multi Dimensional Spatial Data K. Mumtaz 1 Assistant Professor, Department of MCA Vivekanandha Institute of Information and Management Studies, Tiruchengode,

More information

Clustering. Danilo Croce Web Mining & Retrieval a.a. 2015/201 16/03/2016

Clustering. Danilo Croce Web Mining & Retrieval a.a. 2015/201 16/03/2016 Clustering Danilo Croce Web Mining & Retrieval a.a. 2015/201 16/03/2016 1 Supervised learning vs. unsupervised learning Supervised learning: discover patterns in the data that relate data attributes with

More information

Cluster Analysis. Isabel M. Rodrigues. Lisboa, 2014. Instituto Superior Técnico

Cluster Analysis. Isabel M. Rodrigues. Lisboa, 2014. Instituto Superior Técnico Instituto Superior Técnico Lisboa, 2014 Introduction: Cluster analysis What is? Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from

More information

Unsupervised Data Mining (Clustering)

Unsupervised Data Mining (Clustering) Unsupervised Data Mining (Clustering) Javier Béjar KEMLG December 01 Javier Béjar (KEMLG) Unsupervised Data Mining (Clustering) December 01 1 / 51 Introduction Clustering in KDD One of the main tasks in

More information

L15: statistical clustering

L15: statistical clustering Similarity measures Criterion functions Cluster validity Flat clustering algorithms k-means ISODATA L15: statistical clustering Hierarchical clustering algorithms Divisive Agglomerative CSCE 666 Pattern

More information

A comparison of various clustering methods and algorithms in data mining

A comparison of various clustering methods and algorithms in data mining Volume :2, Issue :5, 32-36 May 2015 www.allsubjectjournal.com e-issn: 2349-4182 p-issn: 2349-5979 Impact Factor: 3.762 R.Tamilselvi B.Sivasakthi R.Kavitha Assistant Professor A comparison of various clustering

More information

Clustering Hierarchical clustering and k-mean clustering

Clustering Hierarchical clustering and k-mean clustering Clustering Hierarchical clustering and k-mean clustering Genome 373 Genomic Informatics Elhanan Borenstein The clustering problem: A quick review partition genes into distinct sets with high homogeneity

More information

Summary Data Mining & Process Mining (1BM46) Content. Made by S.P.T. Ariesen

Summary Data Mining & Process Mining (1BM46) Content. Made by S.P.T. Ariesen Summary Data Mining & Process Mining (1BM46) Made by S.P.T. Ariesen Content Data Mining part... 2 Lecture 1... 2 Lecture 2:... 4 Lecture 3... 7 Lecture 4... 9 Process mining part... 13 Lecture 5... 13

More information

Classification algorithm in Data mining: An Overview

Classification algorithm in Data mining: An Overview Classification algorithm in Data mining: An Overview S.Neelamegam #1, Dr.E.Ramaraj *2 #1 M.phil Scholar, Department of Computer Science and Engineering, Alagappa University, Karaikudi. *2 Professor, Department

More information

Data Mining Project Report. Document Clustering. Meryem Uzun-Per

Data Mining Project Report. Document Clustering. Meryem Uzun-Per Data Mining Project Report Document Clustering Meryem Uzun-Per 504112506 Table of Content Table of Content... 2 1. Project Definition... 3 2. Literature Survey... 3 3. Methods... 4 3.1. K-means algorithm...

More information

Data Mining Classification: Alternative Techniques. Instance-Based Classifiers. Lecture Notes for Chapter 5. Introduction to Data Mining

Data Mining Classification: Alternative Techniques. Instance-Based Classifiers. Lecture Notes for Chapter 5. Introduction to Data Mining Data Mining Classification: Alternative Techniques Instance-Based Classifiers Lecture Notes for Chapter 5 Introduction to Data Mining by Tan, Steinbach, Kumar Set of Stored Cases Atr1... AtrN Class A B

More information

Clustering. 15-381 Artificial Intelligence Henry Lin. Organizing data into clusters such that there is

Clustering. 15-381 Artificial Intelligence Henry Lin. Organizing data into clusters such that there is Clustering 15-381 Artificial Intelligence Henry Lin Modified from excellent slides of Eamonn Keogh, Ziv Bar-Joseph, and Andrew Moore What is Clustering? Organizing data into clusters such that there is

More information

Steven M. Ho!and. Department of Geology, University of Georgia, Athens, GA 30602-2501

Steven M. Ho!and. Department of Geology, University of Georgia, Athens, GA 30602-2501 CLUSTER ANALYSIS Steven M. Ho!and Department of Geology, University of Georgia, Athens, GA 30602-2501 January 2006 Introduction Cluster analysis includes a broad suite of techniques designed to find groups

More information

An Ameliorated Partitioning Clustering Algorithm for Large Data Sets

An Ameliorated Partitioning Clustering Algorithm for Large Data Sets An Ameliorated Partitioning Clustering Algorithm for Large Data Sets Raghavi Chouhan 1, Abhishek Chauhan 2 MTech Scholar, CSE department, NRI Institute of Information Science and Technology, Bhopal, India

More information

Concept of Cluster Analysis

Concept of Cluster Analysis RESEARCH PAPER ON CLUSTER TECHNIQUES OF DATA VARIATIONS Er. Arpit Gupta 1,Er.Ankit Gupta 2,Er. Amit Mishra 3 arpit_jp@yahoo.co.in, ank_mgcgv@yahoo.co.in,amitmishra.mtech@gmail.com Faculty Of Engineering

More information

A Novel Density based improved k-means Clustering Algorithm Dbkmeans

A Novel Density based improved k-means Clustering Algorithm Dbkmeans A Novel Density based improved k-means Clustering Algorithm Dbkmeans K. Mumtaz 1 and Dr. K. Duraiswamy 2, 1 Vivekanandha Institute of Information and Management Studies, Tiruchengode, India 2 KS Rangasamy

More information

Social Media Mining. Data Mining Essentials

Social Media Mining. Data Mining Essentials Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers

More information

Clustering & Visualization

Clustering & Visualization Chapter 5 Clustering & Visualization Clustering in high-dimensional databases is an important problem and there are a number of different clustering paradigms which are applicable to high-dimensional data.

More information

SPECIAL PERTURBATIONS UNCORRELATED TRACK PROCESSING

SPECIAL PERTURBATIONS UNCORRELATED TRACK PROCESSING AAS 07-228 SPECIAL PERTURBATIONS UNCORRELATED TRACK PROCESSING INTRODUCTION James G. Miller * Two historical uncorrelated track (UCT) processing approaches have been employed using general perturbations

More information

Data Clustering Techniques Qualifying Oral Examination Paper

Data Clustering Techniques Qualifying Oral Examination Paper Data Clustering Techniques Qualifying Oral Examination Paper Periklis Andritsos University of Toronto Department of Computer Science periklis@cs.toronto.edu March 11, 2002 1 Introduction During a cholera

More information

Classification Techniques (1)

Classification Techniques (1) 10 10 Overview Classification Techniques (1) Today Classification Problem Classification based on Regression Distance-based Classification (KNN) Net Lecture Decision Trees Classification using Rules Quality

More information

Chapter 4: Non-Parametric Classification

Chapter 4: Non-Parametric Classification Chapter 4: Non-Parametric Classification Introduction Density Estimation Parzen Windows Kn-Nearest Neighbor Density Estimation K-Nearest Neighbor (KNN) Decision Rule Gaussian Mixture Model A weighted combination

More information

A Survey of Clustering Techniques

A Survey of Clustering Techniques A Survey of Clustering Techniques Pradeep Rai Asst. Prof., CSE Department, Kanpur Institute of Technology, Kanpur-0800 (India) Shubha Singh Asst. Prof., MCA Department, Kanpur Institute of Technology,

More information

Distance based clustering

Distance based clustering // Distance based clustering Chapter ² ² Clustering Clustering is the art of finding groups in data (Kaufman and Rousseeuw, 99). What is a cluster? Group of objects separated from other clusters Means

More information

Comparison and Analysis of Various Clustering Methods in Data mining On Education data set Using the weak tool

Comparison and Analysis of Various Clustering Methods in Data mining On Education data set Using the weak tool Comparison and Analysis of Various Clustering Metho in Data mining On Education data set Using the weak tool Abstract:- Data mining is used to find the hidden information pattern and relationship between

More information

Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining Knowledge Discovery and Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Evaluating the Accuracy of a Classifier Holdout, random subsampling, crossvalidation, and the bootstrap are common techniques for

More information

Clustering Techniques: A Brief Survey of Different Clustering Algorithms

Clustering Techniques: A Brief Survey of Different Clustering Algorithms Clustering Techniques: A Brief Survey of Different Clustering Algorithms Deepti Sisodia Technocrates Institute of Technology, Bhopal, India Lokesh Singh Technocrates Institute of Technology, Bhopal, India

More information

Hierarchical Cluster Analysis Some Basics and Algorithms

Hierarchical Cluster Analysis Some Basics and Algorithms Hierarchical Cluster Analysis Some Basics and Algorithms Nethra Sambamoorthi CRMportals Inc., 11 Bartram Road, Englishtown, NJ 07726 (NOTE: Please use always the latest copy of the document. Click on this

More information

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015 An Introduction to Data Mining for Wind Power Management Spring 2015 Big Data World Every minute: Google receives over 4 million search queries Facebook users share almost 2.5 million pieces of content

More information

Data visualization and clustering. Genomics is to no small extend a data science

Data visualization and clustering. Genomics is to no small extend a data science Data visualization and clustering Genomics is to no small extend a data science [www.data2discovery.org] Data visualization and clustering Genomics is to no small extend a data science [Andersson et al.,

More information

Data Mining and Knowledge Discovery in Databases (KDD) State of the Art. Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland

Data Mining and Knowledge Discovery in Databases (KDD) State of the Art. Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland Data Mining and Knowledge Discovery in Databases (KDD) State of the Art Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland 1 Conference overview 1. Overview of KDD and data mining 2. Data

More information

Text Clustering. Clustering

Text Clustering. Clustering Text Clustering 1 Clustering Partition unlabeled examples into disoint subsets of clusters, such that: Examples within a cluster are very similar Examples in different clusters are very different Discover

More information

There are a number of different methods that can be used to carry out a cluster analysis; these methods can be classified as follows:

There are a number of different methods that can be used to carry out a cluster analysis; these methods can be classified as follows: Statistics: Rosie Cornish. 2007. 3.1 Cluster Analysis 1 Introduction This handout is designed to provide only a brief introduction to cluster analysis and how it is done. Books giving further details are

More information

Medical Information Management & Mining. You Chen Jan,15, 2013 You.chen@vanderbilt.edu

Medical Information Management & Mining. You Chen Jan,15, 2013 You.chen@vanderbilt.edu Medical Information Management & Mining You Chen Jan,15, 2013 You.chen@vanderbilt.edu 1 Trees Building Materials Trees cannot be used to build a house directly. How can we transform trees to building materials?

More information

Public Transportation BigData Clustering

Public Transportation BigData Clustering Public Transportation BigData Clustering Preliminary Communication Tomislav Galba J.J. Strossmayer University of Osijek Faculty of Electrical Engineering Cara Hadriana 10b, 31000 Osijek, Croatia tomislav.galba@etfos.hr

More information

PCA, Clustering and Classification. By H. Bjørn Nielsen strongly inspired by Agnieszka S. Juncker

PCA, Clustering and Classification. By H. Bjørn Nielsen strongly inspired by Agnieszka S. Juncker PCA, Clustering and Classification By H. Bjørn Nielsen strongly inspired by Agnieszka S. Juncker Motivation: Multidimensional data Pat1 Pat2 Pat3 Pat4 Pat5 Pat6 Pat7 Pat8 Pat9 209619_at 7758 4705 5342

More information

Chapter ML:XI (continued)

Chapter ML:XI (continued) Chapter ML:XI (continued) XI. Cluster Analysis Data Mining Overview Cluster Analysis Basics Hierarchical Cluster Analysis Iterative Cluster Analysis Density-Based Cluster Analysis Cluster Evaluation Constrained

More information

Outlier Detection in Clustering

Outlier Detection in Clustering Outlier Detection in Clustering Svetlana Cherednichenko 24.01.2005 University of Joensuu Department of Computer Science Master s Thesis TABLE OF CONTENTS 1. INTRODUCTION...1 1.1. BASIC DEFINITIONS... 1

More information

Cluster analysis Cosmin Lazar. COMO Lab VUB

Cluster analysis Cosmin Lazar. COMO Lab VUB Cluster analysis Cosmin Lazar COMO Lab VUB Introduction Cluster analysis foundations rely on one of the most fundamental, simple and very often unnoticed ways (or methods) of understanding and learning,

More information

Lecture 20: Clustering

Lecture 20: Clustering Lecture 20: Clustering Wrap-up of neural nets (from last lecture Introduction to unsupervised learning K-means clustering COMP-424, Lecture 20 - April 3, 2013 1 Unsupervised learning In supervised learning,

More information

Data Clustering. Dec 2nd, 2013 Kyrylo Bessonov

Data Clustering. Dec 2nd, 2013 Kyrylo Bessonov Data Clustering Dec 2nd, 2013 Kyrylo Bessonov Talk outline Introduction to clustering Types of clustering Supervised Unsupervised Similarity measures Main clustering algorithms k-means Hierarchical Main

More information

Data Mining 資 料 探 勘. 分 群 分 析 (Cluster Analysis)

Data Mining 資 料 探 勘. 分 群 分 析 (Cluster Analysis) Data Mining 資 料 探 勘 Tamkang University 分 群 分 析 (Cluster Analysis) DM MI Wed,, (:- :) (B) Min-Yuh Day 戴 敏 育 Assistant Professor 專 任 助 理 教 授 Dept. of Information Management, Tamkang University 淡 江 大 學 資

More information

Machine Learning using MapReduce

Machine Learning using MapReduce Machine Learning using MapReduce What is Machine Learning Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous

More information

Unsupervised Learning and Data Mining. Unsupervised Learning and Data Mining. Clustering. Supervised Learning. Supervised Learning

Unsupervised Learning and Data Mining. Unsupervised Learning and Data Mining. Clustering. Supervised Learning. Supervised Learning Unsupervised Learning and Data Mining Unsupervised Learning and Data Mining Clustering Decision trees Artificial neural nets K-nearest neighbor Support vectors Linear regression Logistic regression...

More information

PERFORMANCE ANALYSIS OF CLUSTERING ALGORITHMS IN DATA MINING IN WEKA

PERFORMANCE ANALYSIS OF CLUSTERING ALGORITHMS IN DATA MINING IN WEKA PERFORMANCE ANALYSIS OF CLUSTERING ALGORITHMS IN DATA MINING IN WEKA Prakash Singh 1, Aarohi Surya 2 1 Department of Finance, IIM Lucknow, Lucknow, India 2 Department of Computer Science, LNMIIT, Jaipur,

More information

A Comparative Analysis of Various Clustering Techniques used for Very Large Datasets

A Comparative Analysis of Various Clustering Techniques used for Very Large Datasets A Comparative Analysis of Various Clustering Techniques used for Very Large Datasets Preeti Baser, Assistant Professor, SJPIBMCA, Gandhinagar, Gujarat, India 382 007 Research Scholar, R. K. University,

More information

10-810 /02-710 Computational Genomics. Clustering expression data

10-810 /02-710 Computational Genomics. Clustering expression data 10-810 /02-710 Computational Genomics Clustering expression data What is Clustering? Organizing data into clusters such that there is high intra-cluster similarity low inter-cluster similarity Informally,

More information

Clustering and Cluster Evaluation. Josh Stuart Tuesday, Feb 24, 2004 Read chap 4 in Causton

Clustering and Cluster Evaluation. Josh Stuart Tuesday, Feb 24, 2004 Read chap 4 in Causton Clustering and Cluster Evaluation Josh Stuart Tuesday, Feb 24, 2004 Read chap 4 in Causton Clustering Methods Agglomerative Start with all separate, end with some connected Partitioning / Divisive Start

More information

Machine Learning for NLP

Machine Learning for NLP Natural Language Processing SoSe 2015 Machine Learning for NLP Dr. Mariana Neves May 4th, 2015 (based on the slides of Dr. Saeedeh Momtazi) Introduction Field of study that gives computers the ability

More information

Original Article Survey of Recent Clustering Techniques in Data Mining

Original Article Survey of Recent Clustering Techniques in Data Mining International Archive of Applied Sciences and Technology Volume 3 [2] June 2012: 68-75 ISSN: 0976-4828 Society of Education, India Website: www.soeagra.com/iaast/iaast.htm Original Article Survey of Recent

More information

Robust Outlier Detection Technique in Data Mining: A Univariate Approach

Robust Outlier Detection Technique in Data Mining: A Univariate Approach Robust Outlier Detection Technique in Data Mining: A Univariate Approach Singh Vijendra and Pathak Shivani Faculty of Engineering and Technology Mody Institute of Technology and Science Lakshmangarh, Sikar,

More information

The Role of Visualization in Effective Data Cleaning

The Role of Visualization in Effective Data Cleaning The Role of Visualization in Effective Data Cleaning Yu Qian Dept. of Computer Science The University of Texas at Dallas Richardson, TX 75083-0688, USA qianyu@student.utdallas.edu Kang Zhang Dept. of Computer

More information

Clustering: Techniques & Applications. Nguyen Sinh Hoa, Nguyen Hung Son. 15 lutego 2006 Clustering 1

Clustering: Techniques & Applications. Nguyen Sinh Hoa, Nguyen Hung Son. 15 lutego 2006 Clustering 1 Clustering: Techniques & Applications Nguyen Sinh Hoa, Nguyen Hung Son 15 lutego 2006 Clustering 1 Agenda Introduction Clustering Methods Applications: Outlier Analysis Gene clustering Summary and Conclusions

More information

GE-INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH VOLUME -3, ISSUE-6 (June 2015) IF-4.007 ISSN: (2321-1717) EMERGING CLUSTERING TECHNIQUES ON BIG DATA

GE-INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH VOLUME -3, ISSUE-6 (June 2015) IF-4.007 ISSN: (2321-1717) EMERGING CLUSTERING TECHNIQUES ON BIG DATA EMERGING CLUSTERING TECHNIQUES ON BIG DATA Pooja Batra Nagpal 1, Sarika Chaudhary 2, Preetishree Patnaik 3 1,2,3 Computer Science/Amity University, India ABSTRACT The term "Big Data" defined as enormous

More information

GraphZip: A Fast and Automatic Compression Method for Spatial Data Clustering

GraphZip: A Fast and Automatic Compression Method for Spatial Data Clustering GraphZip: A Fast and Automatic Compression Method for Spatial Data Clustering Yu Qian Kang Zhang Department of Computer Science, The University of Texas at Dallas, Richardson, TX 75083-0688, USA {yxq012100,

More information

On Clustering Validation Techniques

On Clustering Validation Techniques Journal of Intelligent Information Systems, 17:2/3, 107 145, 2001 c 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. On Clustering Validation Techniques MARIA HALKIDI mhalk@aueb.gr YANNIS

More information

Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data

Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data CMPE 59H Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data Term Project Report Fatma Güney, Kübra Kalkan 1/15/2013 Keywords: Non-linear

More information

Statistical Databases and Registers with some datamining

Statistical Databases and Registers with some datamining Unsupervised learning - Statistical Databases and Registers with some datamining a course in Survey Methodology and O cial Statistics Pages in the book: 501-528 Department of Statistics Stockholm University

More information

A Method for Decentralized Clustering in Large Multi-Agent Systems

A Method for Decentralized Clustering in Large Multi-Agent Systems A Method for Decentralized Clustering in Large Multi-Agent Systems Elth Ogston, Benno Overeinder, Maarten van Steen, and Frances Brazier Department of Computer Science, Vrije Universiteit Amsterdam {elth,bjo,steen,frances}@cs.vu.nl

More information

Linköpings Universitet - ITN TNM033 2011-11-30 DBSCAN. A Density-Based Spatial Clustering of Application with Noise

Linköpings Universitet - ITN TNM033 2011-11-30 DBSCAN. A Density-Based Spatial Clustering of Application with Noise DBSCAN A Density-Based Spatial Clustering of Application with Noise Henrik Bäcklund (henba892), Anders Hedblom (andh893), Niklas Neijman (nikne866) 1 1. Introduction Today data is received automatically

More information

Introduction to machine learning and pattern recognition Lecture 1 Coryn Bailer-Jones

Introduction to machine learning and pattern recognition Lecture 1 Coryn Bailer-Jones Introduction to machine learning and pattern recognition Lecture 1 Coryn Bailer-Jones http://www.mpia.de/homes/calj/mlpr_mpia2008.html 1 1 What is machine learning? Data description and interpretation

More information