For supervised classification we have a variety of measures to evaluate how good our model is Accuracy, precision, recall
|
|
|
- Valentine Ellis
- 8 years ago
- Views:
Transcription
1 Cluster Validation
2 Cluster Validit For supervised classification we have a variet of measures to evaluate how good our model is Accurac, precision, recall For cluster analsis, the analogous question is how to evaluate the goodness of the resulting clusters? But clusters are in the ee of the beholder! Then wh do we want to evaluate them? To avoid finding patterns in noise To compare clustering algorithms To compare two sets of clusters To compare two clusters
3 Clusters found in Random Data Random Points DBSCAN K-means Complete Link
4 Different Aspects of Cluster Validation. Determining the clustering tendenc of a set of data, i.e., distinguishing whether non-random structure actuall eists in the data.. Comparing the results of a cluster analsis to eternall known results, e.g., to eternall given class labels. 3. Evaluating how well the results of a cluster analsis fit the data without reference to eternal information. - Use onl the data 4. Comparing the results of two different sets of cluster analses to determine which is better. 5. Determining the correct number of clusters. For, 3, and 4, we can further distinguish whether we want to evaluate the entire clustering or just individual clusters.
5 Measures of Cluster Validit Numerical measures that are applied to judge various aspects of cluster validit, are classified into the following three tpes. Eternal Inde: Used to measure the etent to which cluster labels match eternall supplied class labels. Entrop Internal Inde: Used to measure the goodness of a clustering structure without respect to eternal information. Sum of Squared Error (SSE) Relative Inde: Used to compare two different clusterings or clusters. Often an eternal or internal inde is used for this function, e.g., SSE or entrop Sometimes these are referred to as criteria instead of indices However, sometimes criterion is the general strateg and inde is the numerical measure that implements the criterion.
6 Measuring Cluster Validit Via Correlation Two matrices Proimit Matri Incidence Matri One row and one column for each data point An entr is if the associated pair of points belong to the same cluster An entr is if the associated pair of points belongs to different clusters Compute the correlation between the two matrices Since the matrices are smmetric, onl the correlation between n(n-) / entries needs to be calculated. High correlation indicates that points that belong to the same cluster are close to each other. Not a good measure for some densit or contiguit based clusters.
7 Measuring Cluster Validit Via Correlation Correlation of incidence and proimit matrices for the K-means clusterings of the following two data sets Corr = Corr = -.58
8 Using Similarit Matri for Cluster Validation Order the similarit matri with respect to cluster labels and inspect visuall P o in t s S i m i la r i t P o in t s
9 Using Similarit Matri for Cluster Validation Clusters in random data are not so crisp P o in t s S i m i la r i t P o i n t s DBSCAN
10 Using Similarit Matri for Cluster Validation Clusters in random data are not so crisp P o i n t s S i m i la r i t P o in t s K-means
11 Using Similarit Matri for Cluster Validation Clusters in random data are not so crisp P o in t s S i m i la r i t P o in t s Complete Link
12 Using Similarit Matri for Cluster Validation DBSCAN
13 Internal Measures: SSE Clusters in more complicated figures aren t well separated Internal Inde: Used to measure the goodness of a clustering structure without respect to eternal information SSE SSE is good for comparing two clusterings or two clusters (average SSE). Can also be used to estimate the number of clusters S S E K
14 Internal Measures: SSE SSE curve for a more complicated data set SSE of clusters found using K-means
15 Framework for Cluster Validit Need a framework to interpret an measure. For eample, if our measure of evaluation has the value,, is that good, fair, or poor? Statistics provide a framework for cluster validit The more atpical a clustering result is, the more likel it represents valid structure in the data Can compare the values of an inde that result from random data or clusterings to those of a clustering result. If the value of the inde is unlikel, then the cluster results are valid These approaches are more complicated and harder to understand. For comparing the results of two different sets of cluster analses, a framework is less necessar. However, there is the question of whether the difference between two inde values is significant
16 Statistical Framework for SSE Eample Compare SSE of.5 against three clusters in random data Histogram shows SSE of three clusters in 5 sets of random data points of size distributed over the range..8 for and values C o u n t S S E
17 Statistical Framework for Correlation Correlation of incidence and proimit matrices for the K-means clusterings of the following two data sets Corr = Corr = -.58
18 Internal Measures: Cohesion and Separation Cluster Cohesion: Measures how closel related are objects in a cluster Eample: SSE Cluster Separation: Measure how distinct or wellseparated a cluster is from other clusters Eample: Squared Error Cohesion is measured b the within cluster sum of squares (SSE) WSS = i Separation is measured b the between cluster sum of squares BSS = Ci ( m m i ) Where C i i is the size of cluster i C ( i m i )
19 Internal Measures: Cohesion and Separation Eample: SSE BSS + WSS = constant m m 3 4 m 5 K= cluster: WSS= ( 3) + ( 3) + (4 3) + (5 3) = BSS= 4 (3 3) = Total = + = K= clusters: WSS= (.5) + (.5) + (4 4.5) + (5 4.5) = BSS= Total (3.5) = + 9 = + (4.5 3) = 9
20 Internal Measures: Cohesion and Separation A proimit graph based approach can also be used for cohesion and separation. Cluster cohesion is the sum of the weight of all links within a cluster. Cluster separation is the sum of the weights between nodes in the cluster and nodes outside the cluster. cohesion separation
21 Internal Measures: Silhouette Coefficient Silhouette Coefficient combine ideas of both cohesion and separation, but for individual points, as well as clusters and clusterings For an individual point, i Calculate a = average distance of i to the points in its cluster Calculate b = min (average distance of i to points in another cluster) The silhouette coefficient for a point is then given b s = a/b if a < b, (or s = b/a - Tpicall between and. The closer to the better. if a b, not the usual case) a b Can calculate the Average Silhouette width for a cluster or a clustering
22 Eternal Measures of Cluster Validit: Entrop and Purit
23 Final Comment on Cluster Validit The validation of clustering structures is the most difficult and frustrating part of cluster analsis. Without a strong effort in this direction, cluster analsis will remain a black art accessible onl to those true believers who have eperience and great courage. Algorithms for Clustering Data, Jain and Dubes
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
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
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
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
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,
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
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
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
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
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
Client Based Power Iteration Clustering Algorithm to Reduce Dimensionality in Big Data
Client Based Power Iteration Clustering Algorithm to Reduce Dimensionalit in Big Data Jaalatchum. D 1, Thambidurai. P 1, Department of CSE, PKIET, Karaikal, India Abstract - Clustering is a group of objects
Solving Quadratic Equations by Graphing. Consider an equation of the form. y ax 2 bx c a 0. In an equation of the form
SECTION 11.3 Solving Quadratic Equations b Graphing 11.3 OBJECTIVES 1. Find an ais of smmetr 2. Find a verte 3. Graph a parabola 4. Solve quadratic equations b graphing 5. Solve an application involving
Performance Metrics for Graph Mining Tasks
Performance Metrics for Graph Mining Tasks 1 Outline Introduction to Performance Metrics Supervised Learning Performance Metrics Unsupervised Learning Performance Metrics Optimizing Metrics Statistical
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
Identifying second degree equations
Chapter 7 Identifing second degree equations 7.1 The eigenvalue method In this section we appl eigenvalue methods to determine the geometrical nature of the second degree equation a 2 + 2h + b 2 + 2g +
Chapter 13 Introduction to Linear Regression and Correlation Analysis
Chapter 3 Student Lecture Notes 3- Chapter 3 Introduction to Linear Regression and Correlation Analsis Fall 2006 Fundamentals of Business Statistics Chapter Goals To understand the methods for displaing
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
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
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
Clustering. Clustering. What is Clustering? What is Clustering? What is Clustering? Types of Data in Cluster Analysis
What is Clustering? Clustering Tpes of Data in Cluster Analsis Clustering A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods What is Clustering? Clustering of data is
15.062 Data Mining: Algorithms and Applications Matrix Math Review
.6 Data Mining: Algorithms and Applications Matrix Math Review The purpose of this document is to give a brief review of selected linear algebra concepts that will be useful for the course and to develop
To Be or Not To Be a Linear Equation: That Is the Question
To Be or Not To Be a Linear Equation: That Is the Question Linear Equation in Two Variables A linear equation in two variables is an equation that can be written in the form A + B C where A and B are not
{ } Sec 3.1 Systems of Linear Equations in Two Variables
Sec.1 Sstems of Linear Equations in Two Variables Learning Objectives: 1. Deciding whether an ordered pair is a solution.. Solve a sstem of linear equations using the graphing, substitution, and elimination
MAT188H1S Lec0101 Burbulla
Winter 206 Linear Transformations A linear transformation T : R m R n is a function that takes vectors in R m to vectors in R n such that and T (u + v) T (u) + T (v) T (k v) k T (v), for all vectors u
Solving Systems of Linear Equations With Row Reductions to Echelon Form On Augmented Matrices. Paul A. Trogdon Cary High School Cary, North Carolina
Solving Sstems of Linear Equations With Ro Reductions to Echelon Form On Augmented Matrices Paul A. Trogdon Car High School Car, North Carolina There is no more efficient a to solve a sstem of linear equations
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,
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
STATISTICA. Clustering Techniques. Case Study: Defining Clusters of Shopping Center Patrons. and
Clustering Techniques and STATISTICA Case Study: Defining Clusters of Shopping Center Patrons STATISTICA Solutions for Business Intelligence, Data Mining, Quality Control, and Web-based Analytics Table
SECTION 5-1 Exponential Functions
354 5 Eponential and Logarithmic Functions Most of the functions we have considered so far have been polnomial and rational functions, with a few others involving roots or powers of polnomial or rational
Cross-validation for detecting and preventing overfitting
Cross-validation for detecting and preventing overfitting Note to other teachers and users of these slides. Andrew would be delighted if ou found this source material useful in giving our own lectures.
MBA 611 STATISTICS AND QUANTITATIVE METHODS
MBA 611 STATISTICS AND QUANTITATIVE METHODS Part I. Review of Basic Statistics (Chapters 1-11) A. Introduction (Chapter 1) Uncertainty: Decisions are often based on incomplete information from uncertain
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
Facebook Friend Suggestion Eytan Daniyalzade and Tim Lipus
Facebook Friend Suggestion Eytan Daniyalzade and Tim Lipus 1. Introduction Facebook is a social networking website with an open platform that enables developers to extract and utilize user 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 Outline 1 Cluster Analysis What is Datamining? Cluster Analysis 2 K-means 3 Hierarchical Clustering What is Datamining?
We start with the basic operations on polynomials, that is adding, subtracting, and multiplying.
R. Polnomials In this section we want to review all that we know about polnomials. We start with the basic operations on polnomials, that is adding, subtracting, and multipling. Recall, to add subtract
Pearson s Correlation Coefficient
Pearson s Correlation Coefficient In this lesson, we will find a quantitative measure to describe the strength of a linear relationship (instead of using the terms strong or weak). A quantitative measure
Solution to Homework 2
Solution to Homework 2 Olena Bormashenko September 23, 2011 Section 1.4: 1(a)(b)(i)(k), 4, 5, 14; Section 1.5: 1(a)(b)(c)(d)(e)(n), 2(a)(c), 13, 16, 17, 18, 27 Section 1.4 1. Compute the following, if
STT315 Chapter 4 Random Variables & Probability Distributions KM. Chapter 4.5, 6, 8 Probability Distributions for Continuous Random Variables
Chapter 4.5, 6, 8 Probability Distributions for Continuous Random Variables Discrete vs. continuous random variables Examples of continuous distributions o Uniform o Exponential o Normal Recall: A random
Pennsylvania System of School Assessment
Pennsylvania System of School Assessment The Assessment Anchors, as defined by the Eligible Content, are organized into cohesive blueprints, each structured with a common labeling system that can be read
Java Modules for Time Series Analysis
Java Modules for Time Series Analysis Agenda Clustering Non-normal distributions Multifactor modeling Implied ratings Time series prediction 1. Clustering + Cluster 1 Synthetic Clustering + Time series
K-Means Clustering Tutorial
K-Means Clustering Tutorial By Kardi Teknomo,PhD Preferable reference for this tutorial is Teknomo, Kardi. K-Means Clustering Tutorials. http:\\people.revoledu.com\kardi\ tutorial\kmean\ Last Update: July
Graphing Quadratic Equations
.4 Graphing Quadratic Equations.4 OBJECTIVE. Graph a quadratic equation b plotting points In Section 6.3 ou learned to graph first-degree equations. Similar methods will allow ou to graph quadratic equations
CLUSTER ANALYSIS FOR SEGMENTATION
CLUSTER ANALYSIS FOR SEGMENTATION Introduction We all understand that consumers are not all alike. This provides a challenge for the development and marketing of profitable products and services. Not every
USING SPECTRAL RADIUS RATIO FOR NODE DEGREE TO ANALYZE THE EVOLUTION OF SCALE- FREE NETWORKS AND SMALL-WORLD NETWORKS
USING SPECTRAL RADIUS RATIO FOR NODE DEGREE TO ANALYZE THE EVOLUTION OF SCALE- FREE NETWORKS AND SMALL-WORLD NETWORKS Natarajan Meghanathan Jackson State University, 1400 Lynch St, Jackson, MS, USA natarajan.meghanathan@jsums.edu
The majority of college students hold credit cards. According to the Nellie May
CHAPTER 6 Factoring Polynomials 6.1 The Greatest Common Factor and Factoring by Grouping 6. Factoring Trinomials of the Form b c 6.3 Factoring Trinomials of the Form a b c and Perfect Square Trinomials
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
D.2. The Cartesian Plane. The Cartesian Plane The Distance and Midpoint Formulas Equations of Circles. D10 APPENDIX D Precalculus Review
D0 APPENDIX D Precalculus Review SECTION D. The Cartesian Plane The Cartesian Plane The Distance and Midpoint Formulas Equations of Circles The Cartesian Plane An ordered pair, of real numbers has as its
COC131 Data Mining - Clustering
COC131 Data Mining - Clustering Martin D. Sykora m.d.sykora@lboro.ac.uk Tutorial 05, Friday 20th March 2009 1. Fire up Weka (Waikako Environment for Knowledge Analysis) software, launch the explorer window
Introduction to Matrices for Engineers
Introduction to Matrices for Engineers C.T.J. Dodson, School of Mathematics, Manchester Universit 1 What is a Matrix? A matrix is a rectangular arra of elements, usuall numbers, e.g. 1 0-8 4 0-1 1 0 11
MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Module 7 Test Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. You are given information about a straight line. Use two points to graph the equation.
Decision Support System Methodology Using a Visual Approach for Cluster Analysis Problems
Decision Support System Methodology Using a Visual Approach for Cluster Analysis Problems Ran M. Bittmann School of Business Administration Ph.D. Thesis Submitted to the Senate of Bar-Ilan University Ramat-Gan,
Cluster analysis with SPSS: K-Means Cluster Analysis
analysis with SPSS: K-Means Analysis analysis is a type of data classification carried out by separating the data into groups. The aim of cluster analysis is to categorize n objects in k (k>1) groups,
2. Simple Linear Regression
Research methods - II 3 2. Simple Linear Regression Simple linear regression is a technique in parametric statistics that is commonly used for analyzing mean response of a variable Y which changes according
Downloaded from www.heinemann.co.uk/ib. equations. 2.4 The reciprocal function x 1 x
Functions and equations Assessment statements. Concept of function f : f (); domain, range, image (value). Composite functions (f g); identit function. Inverse function f.. The graph of a function; its
Introduction to Clustering
Introduction to Clustering Yumi Kondo Student Seminar LSK301 Sep 25, 2010 Yumi Kondo (University of British Columbia) Introduction to Clustering Sep 25, 2010 1 / 36 Microarray Example N=65 P=1756 Yumi
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
Session 7 Bivariate Data and Analysis
Session 7 Bivariate Data and Analysis Key Terms for This Session Previously Introduced mean standard deviation New in This Session association bivariate analysis contingency table co-variation least squares
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
Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches
Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches PhD Thesis by Payam Birjandi Director: Prof. Mihai Datcu Problematic
LESSON EIII.E EXPONENTS AND LOGARITHMS
LESSON EIII.E EXPONENTS AND LOGARITHMS LESSON EIII.E EXPONENTS AND LOGARITHMS OVERVIEW Here s what ou ll learn in this lesson: Eponential Functions a. Graphing eponential functions b. Applications of eponential
Find the Relationship: An Exercise in Graphing Analysis
Find the Relationship: An Eercise in Graphing Analsis Computer 5 In several laborator investigations ou do this ear, a primar purpose will be to find the mathematical relationship between two variables.
Exploratory data analysis (Chapter 2) Fall 2011
Exploratory data analysis (Chapter 2) Fall 2011 Data Examples Example 1: Survey Data 1 Data collected from a Stat 371 class in Fall 2005 2 They answered questions about their: gender, major, year in school,
Examples: Joint Densities and Joint Mass Functions Example 1: X and Y are jointly continuous with joint pdf
AMS 3 Joe Mitchell Eamples: Joint Densities and Joint Mass Functions Eample : X and Y are jointl continuous with joint pdf f(,) { c 2 + 3 if, 2, otherwise. (a). Find c. (b). Find P(X + Y ). (c). Find marginal
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
CHAPTER TEN. Key Concepts
CHAPTER TEN Ke Concepts linear regression: slope intercept residual error sum of squares or residual sum of squares sum of squares due to regression mean squares error mean squares regression (coefficient)
Higher. Polynomials and Quadratics 64
hsn.uk.net Higher Mathematics UNIT OUTCOME 1 Polnomials and Quadratics Contents Polnomials and Quadratics 64 1 Quadratics 64 The Discriminant 66 3 Completing the Square 67 4 Sketching Parabolas 70 5 Determining
Decision Trees from large Databases: SLIQ
Decision Trees from large Databases: SLIQ C4.5 often iterates over the training set How often? If the training set does not fit into main memory, swapping makes C4.5 unpractical! SLIQ: Sort the values
CART 6.0 Feature Matrix
CART 6.0 Feature Matri Enhanced Descriptive Statistics Full summary statistics Brief summary statistics Stratified summary statistics Charts and histograms Improved User Interface New setup activity window
Non-negative Matrix Factorization (NMF) in Semi-supervised Learning Reducing Dimension and Maintaining Meaning
Non-negative Matrix Factorization (NMF) in Semi-supervised Learning Reducing Dimension and Maintaining Meaning SAMSI 10 May 2013 Outline Introduction to NMF Applications Motivations NMF as a middle step
2D Geometrical Transformations. Foley & Van Dam, Chapter 5
2D Geometrical Transformations Fole & Van Dam, Chapter 5 2D Geometrical Transformations Translation Scaling Rotation Shear Matri notation Compositions Homogeneous coordinates 2D Geometrical Transformations
Lecture 10: Regression Trees
Lecture 10: Regression Trees 36-350: Data Mining October 11, 2006 Reading: Textbook, sections 5.2 and 10.5. The next three lectures are going to be about a particular kind of nonlinear predictive model,
Polynomial Degree and Finite Differences
CONDENSED LESSON 7.1 Polynomial Degree and Finite Differences In this lesson you will learn the terminology associated with polynomials use the finite differences method to determine the degree of a polynomial
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...
Data Mining and Visualization
Data Mining and Visualization Jeremy Walton NAG Ltd, Oxford Overview Data mining components Functionality Example application Quality control Visualization Use of 3D Example application Market research
Mining Social-Network Graphs
342 Chapter 10 Mining Social-Network Graphs There is much information to be gained by analyzing the large-scale data that is derived from social networks. The best-known example of a social network is
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?
Building Data Cubes and Mining Them. Jelena Jovanovic Email: jeljov@fon.bg.ac.yu
Building Data Cubes and Mining Them Jelena Jovanovic Email: jeljov@fon.bg.ac.yu KDD Process KDD is an overall process of discovering useful knowledge from data. Data mining is a particular step in the
Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.
Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation: - Feature vector X, - qualitative response Y, taking values in C
x y The matrix form, the vector form, and the augmented matrix form, respectively, for the system of equations are
Solving Sstems of Linear Equations in Matri Form with rref Learning Goals Determine the solution of a sstem of equations from the augmented matri Determine the reduced row echelon form of the augmented
Exploratory data analysis for microarray data
Eploratory data analysis for microarray data Anja von Heydebreck Ma Planck Institute for Molecular Genetics, Dept. Computational Molecular Biology, Berlin, Germany heydebre@molgen.mpg.de Visualization
Introduction to Machine Learning Using Python. Vikram Kamath
Introduction to Machine Learning Using Python Vikram Kamath Contents: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. Introduction/Definition Where and Why ML is used Types of Learning Supervised Learning Linear Regression
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
WebFOCUS RStat. RStat. Predict the Future and Make Effective Decisions Today. WebFOCUS RStat
Information Builders enables agile information solutions with business intelligence (BI) and integration technologies. WebFOCUS the most widely utilized business intelligence platform connects to any enterprise
7.3 Parabolas. 7.3 Parabolas 505
7. Parabolas 0 7. Parabolas We have alread learned that the graph of a quadratic function f() = a + b + c (a 0) is called a parabola. To our surprise and delight, we ma also define parabolas in terms of
December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B. KITCHENS
December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B KITCHENS The equation 1 Lines in two-dimensional space (1) 2x y = 3 describes a line in two-dimensional space The coefficients of x and y in the equation
MATH 102 College Algebra
FACTORING Factoring polnomials ls is simpl the reverse process of the special product formulas. Thus, the reverse process of special product formulas will be used to factor polnomials. To factor polnomials
Week 1 Lecture: (1) Course Introduction (2) Data Analysis and Processing
Week 1 Lecture: (1) Course Introduction (2) Data Analysis and Processing January 11, 2016 Welcome, Class! Instructor: C. Thomas Chiou, cchiou@iastate.edu Teaching assistant: Jared Taylor, jaredtay@iastate.edu
Use of Data Mining Techniques to Improve the Effectiveness of Sales and Marketing
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 4, April 2015,
KNIME TUTORIAL. Anna Monreale KDD-Lab, University of Pisa Email: annam@di.unipi.it
KNIME TUTORIAL Anna Monreale KDD-Lab, University of Pisa Email: annam@di.unipi.it Outline Introduction on KNIME KNIME components Exercise: Market Basket Analysis Exercise: Customer Segmentation Exercise:
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
Name Date. Break-Even Analysis
Name Date Break-Even Analsis In our business planning so far, have ou ever asked the questions: How much do I have to sell to reach m gross profit goal? What price should I charge to cover m costs and
Multivariate Analysis of Ecological Data
Multivariate Analysis of Ecological Data MICHAEL GREENACRE Professor of Statistics at the Pompeu Fabra University in Barcelona, Spain RAUL PRIMICERIO Associate Professor of Ecology, Evolutionary Biology
How To Solve The Cluster Algorithm
Cluster Algorithms Adriano Cruz adriano@nce.ufrj.br 28 de outubro de 2013 Adriano Cruz adriano@nce.ufrj.br () Cluster Algorithms 28 de outubro de 2013 1 / 80 Summary 1 K-Means Adriano Cruz adriano@nce.ufrj.br
PROPERTIES OF ELLIPTIC CURVES AND THEIR USE IN FACTORING LARGE NUMBERS
PROPERTIES OF ELLIPTIC CURVES AND THEIR USE IN FACTORING LARGE NUMBERS A ver important set of curves which has received considerabl attention in recent ears in connection with the factoring of large numbers
GRAPH EMPIRICAL MODE DECOMPOSITION. Nicolas Tremblay, Pierre Borgnat, Patrick Flandrin
GRAPH EMPIRICAL MODE DECOMPOSITION Nicolas Trembla, Pierre Borgnat, Patrick Flandrin CNRS, Ecole Normale Supérieure de Lon, Phsics Laborator Lon, France ABSTRACT An etension of Empirical Mode Decomposition
THE POWER RULES. Raising an Exponential Expression to a Power
8 (5-) Chapter 5 Eponents and Polnomials 5. THE POWER RULES In this section Raising an Eponential Epression to a Power Raising a Product to a Power Raising a Quotient to a Power Variable Eponents Summar
The right edge of the box is the third quartile, Q 3, which is the median of the data values above the median. Maximum Median
CONDENSED LESSON 2.1 Box Plots In this lesson you will create and interpret box plots for sets of data use the interquartile range (IQR) to identify potential outliers and graph them on a modified box
MULTIPLE-OBJECTIVE DECISION MAKING TECHNIQUE Analytical Hierarchy Process
MULTIPLE-OBJECTIVE DECISION MAKING TECHNIQUE Analytical Hierarchy Process Business Intelligence and Decision Making Professor Jason Chen The analytical hierarchy process (AHP) is a systematic procedure
Gaussian Probability Density Functions: Properties and Error Characterization
Gaussian Probabilit Densit Functions: Properties and Error Characterization Maria Isabel Ribeiro Institute for Sstems and Robotics Instituto Superior Tcnico Av. Rovisco Pais, 1 149-1 Lisboa PORTUGAL {mir@isr.ist.utl.pt}
f x a 0 n 1 a 0 a 1 cos x a 2 cos 2x a 3 cos 3x b 1 sin x b 2 sin 2x b 3 sin 3x a n cos nx b n sin nx n 1 f x dx y
Fourier Series When the French mathematician Joseph Fourier (768 83) was tring to solve a problem in heat conduction, he needed to epress a function f as an infinite series of sine and cosine functions: