Data Mining - Data. Dr. Jean-Michel RICHER Dr. Jean-Michel RICHER Data Mining - Data 1 / 47
|
|
|
- Hollie Fisher
- 5 years ago
- Views:
Transcription
1 Data Mining - Data Dr. Jean-Michel RICHER 2018 jean-michel.richer@univ-angers.fr Dr. Jean-Michel RICHER Data Mining - Data 1 / 47
2 Outline 1. Introduction 2. Data preprocessing 3. CPA with R 4. Exercise and home work Dr. Jean-Michel RICHER Data Mining - Data 2 / 47
3 1. Introduction Dr. Jean-Michel RICHER Data Mining - Data 3 / 47
4 What we will cover Definition 1 preprocessing of data (normalization, standardization) sampling to train and test classifiers feature selection with PCA Dr. Jean-Michel RICHER Data Mining - Data 4 / 47
5 Raw data collection Raw data collection datasets are not always in good shape different sources of information (Database, Excel,...) missing data, errors a first pre-processing step is needed for example scaling, translation, and rotation of images rearrange, remove rows with empty data or imputate values, i.e. replace missing values using certain statistics (mean, most common,...) Dr. Jean-Michel RICHER Data Mining - Data 5 / 47
6 2. Data preprocessing Dr. Jean-Michel RICHER Data Mining - Data 6 / 47
7 Normalization Normalization Normalization and other feature scaling techniques are often mandatory in order to make comparisons between different attributes if the attributes were measured on different scales (e.g., temperatures in Kelvin and Celsius); the term normalization is often used synonymous to Min-Max scaling The scaling of attributes in a certain range, e.g., 0 to 1: X norm = X X min X max X min Dr. Jean-Michel RICHER Data Mining - Data 7 / 47
8 Standardization Standardization Another common approach is standardization or scaling to unit-variance every sample is subtracted by the attribute s mean µ and divided by the standard deviation σ attributes will have the properties of a standard normal distribution (µ = 0 and σ = 1) this is done in order not to have one property which will have more influence than the others Dr. Jean-Michel RICHER Data Mining - Data 8 / 47
9 Sampling Sampling split the dataset into a training and a test dataset to assess precision of method (classifier) training dataset is used to train the model test dataset evaluate the performance of the final model be aware of overfitting: classifiers that perform well on training data but do not generalize well Dr. Jean-Michel RICHER Data Mining - Data 9 / 47
10 Cross-Validation Cross-Validation most useful techniques to evaluate different combinations of feature (property) selection (take only intersting features) dimensionality reduction of learning algorithms (reduce for efficiency) multiple flavors of cross-validation, most common is k-fold cross-validation Dr. Jean-Michel RICHER Data Mining - Data 10 / 47
11 K-fold cross-validation K-fold the original training dataset is split into k different subsets (the so-called folds) one fold is retained as test set the other k 1 are used for training the model calculate the average error rate (and standard deviation) of the folds use matrix of statistics to see if some fold is different from others Dr. Jean-Michel RICHER Data Mining - Data 11 / 47
12 Feature Selection and Dimensionality Reduction Feature Selection and Dimensionality Reduction the key difference between the terms: feature selection: keep the original feature axis dimensionality reduction: usually involves a transformation technique the main goals are: noise reduction increase computational efficiency by retaining only useful (discriminatory) information avoid overfitting Dr. Jean-Michel RICHER Data Mining - Data 12 / 47
13 Feature Selection Feature Selection retain only those features that are meaningful and help build a good classifier by reducing the number of attributes (features, properties) Linear Discriminant Analysis (LDA): supervised, computes the directions (LD) that maximize the separation between multiple classes Principal Component Analyses (PCA): unsupervised, ignores class labels, find PC that maximize the variance in a dataset Dr. Jean-Michel RICHER Data Mining - Data 13 / 47
14 LDA LDA in a nutshell project a feature space (a dataset of k-dimensional samples) onto a smaller subspace h (where h k 1) while maintaining the class-discriminatory information Dr. Jean-Michel RICHER Data Mining - Data 14 / 47
15 PCA PCA (Principal Component Analysis reduce the dimensionality of a data set consisting of a large number of interrelated variable retain as much as possible the variation in the data set achieved by transforming to a new set of uncorrelated variables (PC) ordered so that the first few retain most of the variation present in all of the original variables Dr. Jean-Michel RICHER Data Mining - Data 15 / 47
16 PCA algorithm (1/2) PCA Algorithm (1/2) Take the whole dataset consisting of k-dimensional samples ignoring the class labels Compute the k-dimensional mean vector (i.e., the means for every dimension of the whole dataset) Compute the scatter matrix (alternatively, the covariance matrix) of the whole data set Compute eigenvectors (e 1, e 2,..., e k ) and corresponding eigenvalues (λ 1, λ 2,..., λ k ) Dr. Jean-Michel RICHER Data Mining - Data 16 / 47
17 PCA algorithm (2/2) PCA Algorithm (2/2) Sort the eigenvectors by decreasing eigenvalues and choose h eigenvectors with the largest eigenvalues to form a k h dimensional matrix W (where every column represents an eigenvector) Use this k h eigenvector matrix to transform the samples onto the new subspace y = W T x (where x is a k 1-dimensional vector representing one sample and y is the transformed h 1-dimensional sample in the new subspace Dr. Jean-Michel RICHER Data Mining - Data 17 / 47
18 PCA algorithm (2/2) Types of PCA general: apply algorithm to data (no transformation of data) centered: substract mean to initial values reduced: first center then reduce (if variances are significantly different, divide each variables by its standard deviation) Dr. Jean-Michel RICHER Data Mining - Data 18 / 47
19 3. PCA with R Dr. Jean-Michel RICHER Data Mining - Data 19 / 47
20 Example of PCA with R Example of PCA with R To have a better understanding of PCA we will use the IRIS flowers dataset 150 individuals (50 Setosa, 50 Versicolour, 50 Virginica) 5 properties: petal length and width sepal length and width (protection for the flower and support for the petals when in bloom) class (Setosa, Versicolour, Virginica) Dr. Jean-Michel RICHER Data Mining - Data 20 / 47
21 Example of PCA with R Dr. Jean-Michel RICHER Data Mining - Data 21 / 47
22 Example of PCA with R View the data Dataset already present > head(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Species setosa setosa setosa setosa setosa setosa Dr. Jean-Michel RICHER Data Mining - Data 22 / 47
23 Example of PCA with R > summary(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Min. :4.300 Min. :2.000 Min. :1.000 Min. : st Qu.: st Qu.: st Qu.: st Qu.:0.300 Median :5.800 Median :3.000 Median :4.350 Median :1.300 Mean :5.843 Mean :3.057 Mean :3.758 Mean : rd Qu.: rd Qu.: rd Qu.: rd Qu.:1.800 Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500 Species setosa :50 versicolor:50 virginica :50 Dr. Jean-Michel RICHER Data Mining - Data 23 / 47
24 Example of PCA with R Plot the data to have a better understanding (with hundreds of attribute this is not feasible) > attach(iris) > plot(iris[,1:4], pch=16) what can we really see? Dr. Jean-Michel RICHER Data Mining - Data 24 / 47
25 Example of PCA with R Dr. Jean-Michel RICHER Data Mining - Data 25 / 47
26 Example of PCA with R Plot in function of classes: > attach(iris) > pairs(iris[1:4],pch = 21, bg = c("red", "green3", "blue")[unclass(iris$species)]) Dr. Jean-Michel RICHER Data Mining - Data 26 / 47
27 Example of PCA with R Dr. Jean-Michel RICHER Data Mining - Data 27 / 47
28 Example of PCA with R In this case if you compare Sepal.Length to Sepal.Width: the red dots are separated from the blue and green dots but it will be difficult to differentiate blue from green if you compare Petal.Length to Petal.Width the differentiation is simple Dr. Jean-Michel RICHER Data Mining - Data 28 / 47
29 Example of PCA with R Correlation matrix values > cor(iris[,1:4]) Sepal.Length Sepal.Width Petal.Length Petal.Width Sepal.Length Sepal.Width Petal.Length Petal.Width Then plot the correlation matrix library(corrplot) corrplot(cor(iris[,1:4]), method="color") Dr. Jean-Michel RICHER Data Mining - Data 29 / 47
30 Example of PCA with R Dr. Jean-Michel RICHER Data Mining - Data 30 / 47
31 Example of PCA with R Correlation explanation if the correlation value is close to 1, then both vectors of values are strongly correlated close to 0, then they are not correlated In this example Sepal.Length and Petal.Length are strongly correlated, so it is interesting to remove one of them Dr. Jean-Michel RICHER Data Mining - Data 31 / 47
32 Example of PCA with R What we want to know is there a difference between the species? which properties can explain thoses differences? Dr. Jean-Michel RICHER Data Mining - Data 32 / 47
33 Example of PCA with R Perform the PCA with R pca = prcomp(iris[,1:4]) The principal component are obtained with pca$rotation Dr. Jean-Michel RICHER Data Mining - Data 33 / 47
34 Example of PCA with R Principal components > pca$rotation PC1 PC2 PC3 PC4 Sepal.Length Sepal.Width Petal.Length Petal.Width Petal.Length Sepal.Length PC1 = Petal.Width 0.08 Sepal.Width Dr. Jean-Michel RICHER Data Mining - Data 34 / 47
35 Example of PCA with R Principal components information pca$sdev gives the pourcentage of information in each component: > pca$sdev [1] > 100 * pca$sdev^2 / sum(pca$sdev^2) [1] > sum(100 * (pca$sdev^2)[1:1] / sum(pca$sdev^2)) [1] > sum(100 * (pca$sdev^2)[1:2] / sum(pca$sdev^2)) [1] > sum(100 * (pca$sdev^2)[1:3] / sum(pca$sdev^2)) [1] > pca$rotation Already 92% of the information is given by the first component! Dr. Jean-Michel RICHER Data Mining - Data 35 / 47
36 Example of PCA with R Dr. Jean-Michel RICHER Data Mining - Data 36 / 47
37 Example of PCA with R Print individuals in function of PC > plot(pca$x[,1], rep(0,nrow(iris)), col=iris[,5]) or > library(ggfortify) > df <- iris[c(1, 2, 3, 4)] > autoplot(prcomp(df), data = iris, colour = 'Species') > autoplot(prcomp(df), data = iris, colour = 'Species', label = TRUE, label.size = 3) Dr. Jean-Michel RICHER Data Mining - Data 37 / 47
38 Example of PCA with R Dr. Jean-Michel RICHER Data Mining - Data 38 / 47
39 Example of PCA with R Dr. Jean-Michel RICHER Data Mining - Data 39 / 47
40 Example of PCA with R Dr. Jean-Michel RICHER Data Mining - Data 40 / 47
41 Example of PCA with R Final interpretation setosa (red) can be identified clearly as the PC 1 is composed of 0.86 Petal.Length, it is the length of the petal that can help differentiate species Dr. Jean-Michel RICHER Data Mining - Data 41 / 47
42 More information about PCA More information Follow the following links practical-guide-principal-component-analysis-python/ Dr. Jean-Michel RICHER Data Mining - Data 42 / 47
43 4. Exercise and home work Dr. Jean-Michel RICHER Data Mining - Data 43 / 47
44 Exercise about PCA PCA with python install the sklearn package and try to do as R display information: data, correlation matrix perform the PCA, print results try to plot data Dr. Jean-Michel RICHER Data Mining - Data 44 / 47
45 Home work about PCA PCA with Weka write a report explaining how to perform PCA using Weka give detail of each step Dr. Jean-Michel RICHER Data Mining - Data 45 / 47
46 4. End Dr. Jean-Michel RICHER Data Mining - Data 46 / 47
47 University of Angers - Faculty of Sciences UA - Angers 2 Boulevard Lavoisier Angers Cedex 01 Tel: (+33) (0) Dr. Jean-Michel RICHER Data Mining - Data 47 / 47
MULTIVARIATE ANALYSIS USING R
MULTIVARIATE ANALYSIS USING R B N Mandal I.A.S.R.I., Library Avenue, New Delhi 110 012 bnmandal @iasri.res.in 1. Introduction This article gives an exposition of how to use the R statistical software for
Work 2. Case-based reasoning exercise
Work 2. Case-based reasoning exercise Marc Albert Garcia Gonzalo, Miquel Perelló Nieto November 19, 2012 1 Introduction In this exercise we have implemented a case-based reasoning system, specifically
k Nearest Neighbors Super simple idea! Instance-based learning as opposed to model-based (no pre-processing)
k Nearest Neighbors k Nearest Neighbors To classify an observation: Look at the labels of some number, say k, of neighboring observations. The observation is then classified based on its nearest neighbors
Introduction to R and Statistical Data Analysis
Microarray Center Introduction to R and Statistical Data Analysis PART II Petr Nazarov petr.nazarov@crp-sante.lu 22-11-2010 OUTLINE PART II Descriptive statistics in R (8) sum, mean, median, sd, var, cor,
Data Mining: Exploring Data. Lecture Notes for Chapter 3
Data Mining: Exploring Data Lecture Notes for Chapter 3 Slides by Tan, Steinbach, Kumar adapted by Michael Hahsler Look for accompanying R code on the course web site. Topics Exploratory Data Analysis
Introduction to Artificial Intelligence
Introduction to Artificial Intelligence COMP307 Machine Learning 2: 3-K Techniques Yi Mei yi.mei@ecs.vuw.ac.nz 1 Outline K-Nearest Neighbour method Classification (Supervised learning) Basic NN (1-NN)
CSC 411: Lecture 14: Principal Components Analysis & Autoencoders
CSC 411: Lecture 14: Principal Components Analysis & Autoencoders Raquel Urtasun & Rich Zemel University of Toronto Nov 4, 2015 Urtasun & Zemel (UofT) CSC 411: 14-PCA & Autoencoders Nov 4, 2015 1 / 18
CSC 411: Lecture 14: Principal Components Analysis & Autoencoders
CSC 411: Lecture 14: Principal Components Analysis & Autoencoders Richard Zemel, Raquel Urtasun and Sanja Fidler University of Toronto Zemel, Urtasun, Fidler (UofT) CSC 411: 14-PCA & Autoencoders 1 / 18
Data Mining: Exploring Data. Lecture Notes for Data Exploration Chapter. Introduction to Data Mining
Data Mining: Exploring Data Lecture Notes for Data Exploration Chapter Introduction to Data Mining by Tan, Steinbach, Karpatne, Kumar 02/03/2018 Introduction to Data Mining 1 What is data exploration?
Practical Data Mining COMP-321B. Tutorial 1: Introduction to the WEKA Explorer
Practical Data Mining COMP-321B Tutorial 1: Introduction to the WEKA Explorer Gabi Schmidberger Mark Hall Richard Kirkby July 12, 2006 c 2006 University of Waikato 1 Setting up your Environment Before
Data analysis case study using R for readily available data set using any one machine learning Algorithm
Assignment-4 Data analysis case study using R for readily available data set using any one machine learning Algorithm Broadly, there are 3 types of Machine Learning Algorithms.. 1. Supervised Learning
Linear discriminant analysis and logistic
Practical 6: classifiers Linear discriminant analysis and logistic This practical looks at two different methods of fitting linear classifiers. The linear discriminant analysis is implemented in the MASS
Dimension Reduction CS534
Dimension Reduction CS534 Why dimension reduction? High dimensionality large number of features E.g., documents represented by thousands of words, millions of bigrams Images represented by thousands of
Data Mining: Exploring Data. Lecture Notes for Chapter 3
Data Mining: Exploring Data Lecture Notes for Chapter 3 1 What is data exploration? A preliminary exploration of the data to better understand its characteristics. Key motivations of data exploration include
In stochastic gradient descent implementations, the fixed learning rate η is often replaced by an adaptive learning rate that decreases over time,
Chapter 2 Although stochastic gradient descent can be considered as an approximation of gradient descent, it typically reaches convergence much faster because of the more frequent weight updates. Since
MUSI-6201 Computational Music Analysis
MUSI-6201 Computational Music Analysis Part 4.3: Feature Post-Processing alexander lerch November 4, 2015 instantaneous features overview text book Chapter 3: Instantaneous Features (pp. 63 69) sources:
Discriminate Analysis
Discriminate Analysis Outline Introduction Linear Discriminant Analysis Examples 1 Introduction What is Discriminant Analysis? Statistical technique to classify objects into mutually exclusive and exhaustive
Machine Learning: Algorithms and Applications Mockup Examination
Machine Learning: Algorithms and Applications Mockup Examination 14 May 2012 FIRST NAME STUDENT NUMBER LAST NAME SIGNATURE Instructions for students Write First Name, Last Name, Student Number and Signature
Data Mining: Exploring Data. Lecture Notes for Chapter 3. Introduction to Data Mining
Data Mining: Exploring Data Lecture Notes for Chapter 3 Introduction to Data Mining by Tan, Steinbach, Kumar What is data exploration? A preliminary exploration of the data to better understand its characteristics.
K-means Clustering & PCA
K-means Clustering & PCA Andreas C. Kapourani (Credit: Hiroshi Shimodaira) 02 February 2018 1 Introduction In this lab session we will focus on K-means clustering and Principal Component Analysis (PCA).
Decision Trees In Weka,Data Formats
CS 4510/9010 Applied Machine Learning 1 Decision Trees In Weka,Data Formats Paula Matuszek Fall, 2016 J48: Decision Tree in Weka 2 NAME: weka.classifiers.trees.j48 SYNOPSIS Class for generating a pruned
Network Traffic Measurements and Analysis
DEIB - Politecnico di Milano Fall, 2017 Introduction Often, we have only a set of features x = x 1, x 2,, x n, but no associated response y. Therefore we are not interested in prediction nor classification,
Hands on Datamining & Machine Learning with Weka
Step1: Click the Experimenter button to launch the Weka Experimenter. The Weka Experimenter allows you to design your own experiments of running algorithms on datasets, run the experiments and analyze
Data Mining: Unsupervised Learning. Business Analytics Practice Winter Term 2015/16 Stefan Feuerriegel
Data Mining: Unsupervised Learning Business Analytics Practice Winter Term 2015/16 Stefan Feuerriegel Today s Lecture Objectives 1 Learning how k-means clustering works 2 Understanding dimensionality reduction
University of Florida CISE department Gator Engineering. Visualization
Visualization Dr. Sanjay Ranka Professor Computer and Information Science and Engineering University of Florida What is visualization? Visualization is the process of converting data (information) in to
Clustering and Visualisation of Data
Clustering and Visualisation of Data Hiroshi Shimodaira January-March 28 Cluster analysis aims to partition a data set into meaningful or useful groups, based on distances between data points. In some
Introduction to Machine Learning Prof. Anirban Santara Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur
Introduction to Machine Learning Prof. Anirban Santara Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur Lecture 14 Python Exercise on knn and PCA Hello everyone,
arulescba: Classification for Factor and Transactional Data Sets Using Association Rules
arulescba: Classification for Factor and Transactional Data Sets Using Association Rules Ian Johnson Southern Methodist University Abstract This paper presents an R package, arulescba, which uses association
Input: Concepts, Instances, Attributes
Input: Concepts, Instances, Attributes 1 Terminology Components of the input: Concepts: kinds of things that can be learned aim: intelligible and operational concept description Instances: the individual,
Data Exploration and Preparation Data Mining and Text Mining (UIC Politecnico di Milano)
Data Exploration and Preparation Data Mining and Text Mining (UIC 583 @ Politecnico di Milano) References Jiawei Han and Micheline Kamber, "Data Mining, : Concepts and Techniques", The Morgan Kaufmann
Analysis and Latent Semantic Indexing
18 Principal Component Analysis and Latent Semantic Indexing Understand the basics of principal component analysis and latent semantic index- Lab Objective: ing. Principal Component Analysis Understanding
Data preprocessing Functional Programming and Intelligent Algorithms
Data preprocessing Functional Programming and Intelligent Algorithms Que Tran Høgskolen i Ålesund 20th March 2017 1 Why data preprocessing? Real-world data tend to be dirty incomplete: lacking attribute
Linear Discriminant Analysis in Ottoman Alphabet Character Recognition
Linear Discriminant Analysis in Ottoman Alphabet Character Recognition ZEYNEB KURT, H. IREM TURKMEN, M. ELIF KARSLIGIL Department of Computer Engineering, Yildiz Technical University, 34349 Besiktas /
Cluster Analysis and Visualization. Workshop on Statistics and Machine Learning 2004/2/6
Cluster Analysis and Visualization Workshop on Statistics and Machine Learning 2004/2/6 Outlines Introduction Stages in Clustering Clustering Analysis and Visualization One/two-dimensional Data Histogram,
Intro to R for Epidemiologists
Lab 9 (3/19/15) Intro to R for Epidemiologists Part 1. MPG vs. Weight in mtcars dataset The mtcars dataset in the datasets package contains fuel consumption and 10 aspects of automobile design and performance
EPL451: Data Mining on the Web Lab 10
EPL451: Data Mining on the Web Lab 10 Παύλος Αντωνίου Γραφείο: B109, ΘΕΕ01 University of Cyprus Department of Computer Science Dimensionality Reduction Map points in high-dimensional (high-feature) space
STAT 1291: Data Science
STAT 1291: Data Science Lecture 18 - Statistical modeling II: Machine learning Sungkyu Jung Where are we? data visualization data wrangling professional ethics statistical foundation Statistical modeling:
UNSUPERVISED LEARNING IN PYTHON. Visualizing the PCA transformation
UNSUPERVISED LEARNING IN PYTHON Visualizing the PCA transformation Dimension reduction More efficient storage and computation Remove less-informative "noise" features... which cause problems for prediction
Image Processing. Image Features
Image Processing Image Features Preliminaries 2 What are Image Features? Anything. What they are used for? Some statements about image fragments (patches) recognition Search for similar patches matching
The Curse of Dimensionality
The Curse of Dimensionality ACAS 2002 p1/66 Curse of Dimensionality The basic idea of the curse of dimensionality is that high dimensional data is difficult to work with for several reasons: Adding more
CS 8520: Artificial Intelligence. Weka Lab. Paula Matuszek Fall, CSC 8520 Fall Paula Matuszek
CS 8520: Artificial Intelligence Weka Lab Paula Matuszek Fall, 2015!1 Weka is Waikato Environment for Knowledge Analysis Machine Learning Software Suite from the University of Waikato Been under development
KTH ROYAL INSTITUTE OF TECHNOLOGY. Lecture 14 Machine Learning. K-means, knn
KTH ROYAL INSTITUTE OF TECHNOLOGY Lecture 14 Machine Learning. K-means, knn Contents K-means clustering K-Nearest Neighbour Power Systems Analysis An automated learning approach Understanding states in
Performance Analysis of Data Mining Classification Techniques
Performance Analysis of Data Mining Classification Techniques Tejas Mehta 1, Dr. Dhaval Kathiriya 2 Ph.D. Student, School of Computer Science, Dr. Babasaheb Ambedkar Open University, Gujarat, India 1 Principal
BL5229: Data Analysis with Matlab Lab: Learning: Clustering
BL5229: Data Analysis with Matlab Lab: Learning: Clustering The following hands-on exercises were designed to teach you step by step how to perform and understand various clustering algorithm. We will
EPL451: Data Mining on the Web Lab 5
EPL451: Data Mining on the Web Lab 5 Παύλος Αντωνίου Γραφείο: B109, ΘΕΕ01 University of Cyprus Department of Computer Science Predictive modeling techniques IBM reported in June 2012 that 90% of data available
DATA MINING TEST 2 INSTRUCTIONS: this test consists of 4 questions you may attempt all questions. maximum marks = 100 bonus marks available = 10
COMP717, Data Mining with R, Test Two, Tuesday the 28 th of May, 2013, 8h30-11h30 1 DATA MINING TEST 2 INSTRUCTIONS: this test consists of 4 questions you may attempt all questions. maximum marks = 100
CSCI567 Machine Learning (Fall 2014)
CSCI567 Machine Learning (Fall 2014) Drs. Sha & Liu {feisha,yanliu.cs}@usc.edu September 9, 2014 Drs. Sha & Liu ({feisha,yanliu.cs}@usc.edu) CSCI567 Machine Learning (Fall 2014) September 9, 2014 1 / 47
Instance-Based Representations. k-nearest Neighbor. k-nearest Neighbor. k-nearest Neighbor. exemplars + distance measure. Challenges.
Instance-Based Representations exemplars + distance measure Challenges. algorithm: IB1 classify based on majority class of k nearest neighbors learned structure is not explicitly represented choosing k
2. Data Preprocessing
2. Data Preprocessing Contents of this Chapter 2.1 Introduction 2.2 Data cleaning 2.3 Data integration 2.4 Data transformation 2.5 Data reduction Reference: [Han and Kamber 2006, Chapter 2] SFU, CMPT 459
Inf2B assignment 2. Natural images classification. Hiroshi Shimodaira and Pol Moreno. Submission due: 4pm, Wednesday 30 March 2016.
Inf2B assignment 2 (Ver. 1.2) Natural images classification Submission due: 4pm, Wednesday 30 March 2016 Hiroshi Shimodaira and Pol Moreno This assignment is out of 100 marks and forms 12.5% of your final
Data Mining Chapter 3: Visualizing and Exploring Data Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University
Data Mining Chapter 3: Visualizing and Exploring Data Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University Exploratory data analysis tasks Examine the data, in search of structures
Graphing Bivariate Relationships
Graphing Bivariate Relationships Overview To fully explore the relationship between two variables both summary statistics and visualizations are important. For this assignment you will describe the relationship
Classification with Diffuse or Incomplete Information
Classification with Diffuse or Incomplete Information AMAURY CABALLERO, KANG YEN Florida International University Abstract. In many different fields like finance, business, pattern recognition, communication
netzen - a software tool for the analysis and visualization of network data about
Architect and main contributor: Dr. Carlos D. Correa Other contributors: Tarik Crnovrsanin and Yu-Hsuan Chan PI: Dr. Kwan-Liu Ma Visualization and Interface Design Innovation (ViDi) research group Computer
K-fold cross validation in the Tidyverse Stephanie J. Spielman 11/7/2017
K-fold cross validation in the Tidyverse Stephanie J. Spielman 11/7/2017 Requirements This demo requires several packages: tidyverse (dplyr, tidyr, tibble, ggplot2) modelr broom proc Background K-fold
ADaM version 4.0 (Eagle) Tutorial Information Technology and Systems Center University of Alabama in Huntsville
ADaM version 4.0 (Eagle) Tutorial Information Technology and Systems Center University of Alabama in Huntsville Tutorial Outline Overview of the Mining System Architecture Data Formats Components Using
Feature Selection. CE-725: Statistical Pattern Recognition Sharif University of Technology Spring Soleymani
Feature Selection CE-725: Statistical Pattern Recognition Sharif University of Technology Spring 2013 Soleymani Outline Dimensionality reduction Feature selection vs. feature extraction Filter univariate
Hsiaochun Hsu Date: 12/12/15. Support Vector Machine With Data Reduction
Support Vector Machine With Data Reduction 1 Table of Contents Summary... 3 1. Introduction of Support Vector Machines... 3 1.1 Brief Introduction of Support Vector Machines... 3 1.2 SVM Simple Experiment...
A Course in Machine Learning
A Course in Machine Learning Hal Daumé III 13 UNSUPERVISED LEARNING If you have access to labeled training data, you know what to do. This is the supervised setting, in which you have a teacher telling
CSE 255 Lecture 5. Data Mining and Predictive Analytics. Dimensionality Reduction
CSE 255 Lecture 5 Data Mining and Predictive Analytics Dimensionality Reduction Course outline Week 4: I ll cover homework 1, and get started on Recommender Systems Week 5: I ll cover homework 2 (at the
Statistical Analysis of Metabolomics Data. Xiuxia Du Department of Bioinformatics & Genomics University of North Carolina at Charlotte
Statistical Analysis of Metabolomics Data Xiuxia Du Department of Bioinformatics & Genomics University of North Carolina at Charlotte Outline Introduction Data pre-treatment 1. Normalization 2. Centering,
Biology Project 1
Biology 6317 Project 1 Data and illustrations courtesy of Professor Tony Frankino, Department of Biology/Biochemistry 1. Background The data set www.math.uh.edu/~charles/wing_xy.dat has measurements related
Nearest Neighbor Classification
Nearest Neighbor Classification Professor Ameet Talwalkar Professor Ameet Talwalkar CS260 Machine Learning Algorithms January 11, 2017 1 / 48 Outline 1 Administration 2 First learning algorithm: Nearest
DATA VISUALIZATION WITH GGPLOT2. Coordinates
DATA VISUALIZATION WITH GGPLOT2 Coordinates Coordinates Layer Controls plot dimensions coord_ coord_cartesian() Zooming in scale_x_continuous(limits =...) xlim() coord_cartesian(xlim =...) Original Plot
Lecture 25: Review I
Lecture 25: Review I Reading: Up to chapter 5 in ISLR. STATS 202: Data mining and analysis Jonathan Taylor 1 / 18 Unsupervised learning In unsupervised learning, all the variables are on equal standing,
Machine Learning for. Artem Lind & Aleskandr Tkachenko
Machine Learning for Object Recognition Artem Lind & Aleskandr Tkachenko Outline Problem overview Classification demo Examples of learning algorithms Probabilistic modeling Bayes classifier Maximum margin
Computational Statistics The basics of maximum likelihood estimation, Bayesian estimation, object recognitions
Computational Statistics The basics of maximum likelihood estimation, Bayesian estimation, object recognitions Thomas Giraud Simon Chabot October 12, 2013 Contents 1 Discriminant analysis 3 1.1 Main idea................................
DATA ANALYSIS I. Types of Attributes Sparse, Incomplete, Inaccurate Data
DATA ANALYSIS I Types of Attributes Sparse, Incomplete, Inaccurate Data Sources Bramer, M. (2013). Principles of data mining. Springer. [12-21] Witten, I. H., Frank, E. (2011). Data Mining: Practical machine
Experimental Design + k- Nearest Neighbors
10-601 Introduction to Machine Learning Machine Learning Department School of Computer Science Carnegie Mellon University Experimental Design + k- Nearest Neighbors KNN Readings: Mitchell 8.2 HTF 13.3
Introduction to Machine Learning
Introduction to Machine Learning Eric Medvet 16/3/2017 1/77 Outline Machine Learning: what and why? Motivating example Tree-based methods Regression trees Trees aggregation 2/77 Teachers Eric Medvet Dipartimento
Basic Concepts Weka Workbench and its terminology
Changelog: 14 Oct, 30 Oct Basic Concepts Weka Workbench and its terminology Lecture Part Outline Concepts, instances, attributes How to prepare the input: ARFF, attributes, missing values, getting to know
HW Assignment 3 (Due by 9:00am on Mar 6)
HW Assignment 3 (Due by 9:00am on Mar 6) 1 Theory (150 points) 1. [Tied Weights, 50 points] Write down the gradient computation for a (non-linear) auto-encoder with tied weights i.e., W (2) = (W (1) )
Introduction to R for Epidemiologists
Introduction to R for Epidemiologists Jenna Krall, PhD Thursday, January 29, 2015 Final project Epidemiological analysis of real data Must include: Summary statistics T-tests or chi-squared tests Regression
CS 195-5: Machine Learning Problem Set 5
CS 195-5: Machine Learning Problem Set 5 Douglas Lanman dlanman@brown.edu 26 November 26 1 Clustering and Vector Quantization Problem 1 Part 1: In this problem we will apply Vector Quantization (VQ) to
Image processing & Computer vision Xử lí ảnh và thị giác máy tính
Image processing & Computer vision Xử lí ảnh và thị giác máy tính Detection and Recognition 2D et 3D Alain Boucher - IFI Introduction In this chapter, we introduce some techniques for pattern detection
Applied Neuroscience. Columbia Science Honors Program Fall Machine Learning and Neural Networks
Applied Neuroscience Columbia Science Honors Program Fall 2016 Machine Learning and Neural Networks Machine Learning and Neural Networks Objective: Introduction to Machine Learning Agenda: 1. JavaScript
General Instructions. Questions
CS246: Mining Massive Data Sets Winter 2018 Problem Set 2 Due 11:59pm February 8, 2018 Only one late period is allowed for this homework (11:59pm 2/13). General Instructions Submission instructions: These
Data Mining: Data. Lecture Notes for Chapter 2. Introduction to Data Mining
Data Mining: Data Lecture Notes for Chapter 2 Introduction to Data Mining by Tan, Steinbach, Kumar Data Preprocessing Aggregation Sampling Dimensionality Reduction Feature subset selection Feature creation
CSE 481C Imitation Learning in Humanoid Robots Motion capture, inverse kinematics, and dimensionality reduction
1 CSE 481C Imitation Learning in Humanoid Robots Motion capture, inverse kinematics, and dimensionality reduction Robotic Imitation of Human Actions 2 The inverse kinematics problem Joint angles Human-robot
An Introduction to Cluster Analysis. Zhaoxia Yu Department of Statistics Vice Chair of Undergraduate Affairs
An Introduction to Cluster Analysis Zhaoxia Yu Department of Statistics Vice Chair of Undergraduate Affairs zhaoxia@ics.uci.edu 1 What can you say about the figure? signal C 0.0 0.5 1.0 1500 subjects Two
Recognition: Face Recognition. Linda Shapiro EE/CSE 576
Recognition: Face Recognition Linda Shapiro EE/CSE 576 1 Face recognition: once you ve detected and cropped a face, try to recognize it Detection Recognition Sally 2 Face recognition: overview Typical
Face detection and recognition. Many slides adapted from K. Grauman and D. Lowe
Face detection and recognition Many slides adapted from K. Grauman and D. Lowe Face detection and recognition Detection Recognition Sally History Early face recognition systems: based on features and distances
Manuel Oviedo de la Fuente and Manuel Febrero Bande
Supervised classification methods in by fda.usc package Manuel Oviedo de la Fuente and Manuel Febrero Bande Universidade de Santiago de Compostela CNTG (Centro de Novas Tecnoloxías de Galicia). Santiago
Cluster Analysis. Mu-Chun Su. Department of Computer Science and Information Engineering National Central University 2003/3/11 1
Cluster Analysis Mu-Chun Su Department of Computer Science and Information Engineering National Central University 2003/3/11 1 Introduction Cluster analysis is the formal study of algorithms and methods
Boxplot
Boxplot By: Meaghan Petix, Samia Porto & Franco Porto A boxplot is a convenient way of graphically depicting groups of numerical data through their five number summaries: the smallest observation (sample
刘淇 School of Computer Science and Technology USTC
Data Exploration 刘淇 School of Computer Science and Technology USTC http://staff.ustc.edu.cn/~qiliuql/dm2013.html t t / l/dm2013 l What is data exploration? A preliminary exploration of the data to better
Visualizing high-dimensional data:
Visualizing high-dimensional data: Applying graph theory to data visualization Wayne Oldford based on joint work with Catherine Hurley (Maynooth, Ireland) Adrian Waddell (Waterloo, Canada) Challenge p
Exploratory data analysis for microarrays
Exploratory data analysis for microarrays Jörg Rahnenführer Computational Biology and Applied Algorithmics Max Planck Institute for Informatics D-66123 Saarbrücken Germany NGFN - Courses in Practical DNA
Facial Expression Detection Using Implemented (PCA) Algorithm
Facial Expression Detection Using Implemented (PCA) Algorithm Dileep Gautam (M.Tech Cse) Iftm University Moradabad Up India Abstract: Facial expression plays very important role in the communication with
LaTeX packages for R and Advanced knitr
LaTeX packages for R and Advanced knitr Iowa State University April 9, 2014 More ways to combine R and LaTeX Additional knitr options for formatting R output: \Sexpr{}, results='asis' xtable - formats
Data Mining. CS57300 Purdue University. Bruno Ribeiro. February 1st, 2018
Data Mining CS57300 Purdue University Bruno Ribeiro February 1st, 2018 1 Exploratory Data Analysis & Feature Construction How to explore a dataset Understanding the variables (values, ranges, and empirical
3. Data Preprocessing. 3.1 Introduction
3. Data Preprocessing Contents of this Chapter 3.1 Introduction 3.2 Data cleaning 3.3 Data integration 3.4 Data transformation 3.5 Data reduction SFU, CMPT 740, 03-3, Martin Ester 84 3.1 Introduction Motivation
6 Subscripting. 6.1 Basics of Subscripting. 6.2 Numeric Subscripts. 6.3 Character Subscripts
6 Subscripting 6.1 Basics of Subscripting For objects that contain more than one element (vectors, matrices, arrays, data frames, and lists), subscripting is used to access some or all of those elements.
10/14/2017. Dejan Sarka. Anomaly Detection. Sponsors
Dejan Sarka Anomaly Detection Sponsors About me SQL Server MVP (17 years) and MCT (20 years) 25 years working with SQL Server Authoring 16 th book Authoring many courses, articles Agenda Introduction Simple
Programming Exercise 7: K-means Clustering and Principal Component Analysis
Programming Exercise 7: K-means Clustering and Principal Component Analysis Machine Learning May 13, 2012 Introduction In this exercise, you will implement the K-means clustering algorithm and apply it
Unsupervised Learning
Unsupervised Learning Learning without Class Labels (or correct outputs) Density Estimation Learn P(X) given training data for X Clustering Partition data into clusters Dimensionality Reduction Discover
Basics of Multivariate Modelling and Data Analysis
Basics of Multivariate Modelling and Data Analysis Kurt-Erik Häggblom 9. Linear regression with latent variables 9.1 Principal component regression (PCR) 9.2 Partial least-squares regression (PLS) [ mostly
Chapter 3: Data Description - Part 3. Homework: Exercises 1-21 odd, odd, odd, 107, 109, 118, 119, 120, odd
Chapter 3: Data Description - Part 3 Read: Sections 1 through 5 pp 92-149 Work the following text examples: Section 3.2, 3-1 through 3-17 Section 3.3, 3-22 through 3.28, 3-42 through 3.82 Section 3.4,
ECE 285 Class Project Report
ECE 285 Class Project Report Based on Source localization in an ocean waveguide using supervised machine learning Yiwen Gong ( yig122@eng.ucsd.edu), Yu Chai( yuc385@eng.ucsd.edu ), Yifeng Bu( ybu@eng.ucsd.edu
K-means clustering Based in part on slides from textbook, slides of Susan Holmes. December 2, Statistics 202: Data Mining.
K-means clustering Based in part on slides from textbook, slides of Susan Holmes December 2, 2012 1 / 1 K-means Outline K-means, K-medoids Choosing the number of clusters: Gap test, silhouette plot. Mixture