Data Mining. Part 1. Introduction. 1.4 Input. Spring Instructor: Dr. Masoud Yaghini. Input
|
|
|
- Vincent Fletcher
- 5 years ago
- Views:
Transcription
1 Data Mining Part 1. Introduction 1.4 Spring 2010 Instructor: Dr. Masoud Yaghini
2 Outline Instances Attributes References
3 Instances
4 Instance: Instances Individual, independent example of the concept to be learned. Characterized by a predetermined set of attributes to learning process: set of instances/dataset Each dataset is represented as a matrix of instances versus attributes Represented as a single table or flat file Rather restricted form of input No relationships between objects Problems often involve relationships between objects rather than separate, independent instances.
5 Example: An example: A family tree a family tree is given, and we want to learn the concept sister. This tree is the input to the learning process, along with a list of pairs of people and an indication of whether they are sisters or not.
6 Two ways of expressing the sister-of relation Neither table is of any use without the family tree itself.
7 Family tree represented as a table These tables do not contain independent sets of instances because values in the Name, Parent1, and Parent2 columns refer to rows of the family tree relation.
8 The sister-of relation represented in a table Each of instance is an individual, independent example of the concept that is to be learned.
9 A simple rule for the sister-of relation A simple rule for the sister-of relation is as follows:
10 Denormalization Denormalization or flattening: Several relations are joined together to make one to recast data into a set of independent instances Possible with any finite set of finite relations Problem: Denormalization may produce false regularities that reflect structure of database Example: supplier predicts supplier address
11 Instances The input to a data mining scheme is generally expressed as a table of independent instances of the concept to be learned. The instances are the rows of the tables the attributes are the columns.
12 Attributes
13 Attributes Each instance is described by a fixed predefined set of features or attributes Problem: Number of attributes may vary in different instances Example: the instances were transportation vehicles Possible solution: to make each possible feature an attribute and to use a special flag value to indicate that a particular attribute is not available for a particular case.
14 Attributes Another problem: existence of an attribute may depend of value of another one Spouse s name depends on the value of married or single attribute
15 Attributes Types Possible attribute types (levels of measurement): Numeric (Interval-scaled) quantities Binary (Boolean) quantities Categorical (Nominal) quantities Ordinal quantities
16 Numeric Quantities Numeric (Interval-scaled) quantities are continuous measurements of a roughly linear scale. Numeric quantities are not only ordered but measured in fixed and equal units Example 1: attribute temperature expressed in degrees Fahrenheit Example 2: attribute date (year) Difference of two values makes sense
17 Binary Quantities A Binary (Boolean) quantity can take has only two states: 0 or 1, where 0 means that the variable is absent, and 1 means that it is present. Given the variable smoker describing a patient, 1 indicates that the patient smokes 0 indicates that the patient does not.
18 Categorical Quantities Categorical (Nominal) attributes take on values in a prespecified, finite set of possibilities. Categorical quantities values are distinct symbols Values themselves serve only as labels or names Example: attribute outlook from weather data Values: sunny, overcast, and rainy No relation is implied among categorical values (no ordering or distance measure) Special case: binary attribute Example: true/false or yes / no
19 Categorical Quantities Note: addition, subtraction, and comparing don t make sense Only equality tests can be performed Example:
20 Ordinal quantities Ordinal quantities are ones that make it possible to rank order the categories. But: no distance between values defined Example: attribute temperature in weather data Or: hot > mild > cool Note: it makes sense to compare two values, but addition and subtraction don t make sense Example rule: temperature < hot => play = yes Distinction between nominal and ordinal not always clear (e.g. attribute outlook )
21 References
22 References Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition, Elsevier Inc., (Chapter 2)
23 The end
Data Mining. Part 1. Introduction. 1.3 Input. Fall Instructor: Dr. Masoud Yaghini. Input
Data Mining Part 1. Introduction 1.3 Fall 2009 Instructor: Dr. Masoud Yaghini Outline Instances Attributes References Instances Instance: Instances Individual, independent example of the concept to be
Data Mining. 1.3 Input. Fall Instructor: Dr. Masoud Yaghini. Chapter 3: Input
Data Mining 1.3 Input Fall 2008 Instructor: Dr. Masoud Yaghini Outline Instances Attributes References Instances Instance: Instances Individual, independent example of the concept to be learned. Characterized
Data Mining. Practical Machine Learning Tools and Techniques. Slides for Chapter 3 of Data Mining by I. H. Witten, E. Frank and M. A.
Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 3 of Data Mining by I. H. Witten, E. Frank and M. A. Hall Input: Concepts, instances, attributes Terminology What s a concept?
Machine Learning Chapter 2. Input
Machine Learning Chapter 2. Input 2 Input: Concepts, instances, attributes Terminology What s a concept? Classification, association, clustering, numeric prediction What s in an example? Relations, flat
Data Mining Practical Machine Learning Tools and Techniques
Input: Concepts, instances, attributes Data ining Practical achine Learning Tools and Techniques Slides for Chapter 2 of Data ining by I. H. Witten and E. rank Terminology What s a concept z Classification,
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,
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
Normalization and denormalization Missing values Outliers detection and removing Noisy Data Variants of Attributes Meta Data Data Transformation
Preprocessing Data Normalization and denormalization Missing values Outliers detection and removing Noisy Data Variants of Attributes Meta Data Data Transformation Reading material: Chapters 2 and 3 of
Data Mining Input: Concepts, Instances, and Attributes
Data Mining Input: Concepts, Instances, and Attributes Chapter 2 of Data Mining Terminology Components of the input: Concepts: kinds of things that can be learned Goal: intelligible and operational concept
Representing structural patterns: Reading Material: Chapter 3 of the textbook by Witten
Representing structural patterns: Plain Classification rules Decision Tree Rules with exceptions Relational solution Tree for Numerical Prediction Instance-based presentation Reading Material: Chapter
CS513-Data Mining. Lecture 2: Understanding the Data. Waheed Noor
CS513-Data Mining Lecture 2: Understanding the Data Waheed Noor Computer Science and Information Technology, University of Balochistan, Quetta, Pakistan Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining
Data Mining. 3.5 Lazy Learners (Instance-Based Learners) Fall Instructor: Dr. Masoud Yaghini. Lazy Learners
Data Mining 3.5 (Instance-Based Learners) Fall 2008 Instructor: Dr. Masoud Yaghini Outline Introduction k-nearest-neighbor Classifiers References Introduction Introduction Lazy vs. eager learning Eager
WEKA: Practical Machine Learning Tools and Techniques in Java. Seminar A.I. Tools WS 2006/07 Rossen Dimov
WEKA: Practical Machine Learning Tools and Techniques in Java Seminar A.I. Tools WS 2006/07 Rossen Dimov Overview Basic introduction to Machine Learning Weka Tool Conclusion Document classification Demo
Data Representation Information Retrieval and Data Mining. Prof. Matteo Matteucci
Data Representation Information Retrieval and Data Mining Prof. Matteo Matteucci Instances, Attributes, Concepts 2 Instances The atomic elements of information from a dataset Also known as records, prototypes,
Data Mining Practical Machine Learning Tools and Techniques
Output: Knowledge representation Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter of Data Mining by I. H. Witten and E. Frank Decision tables Decision trees Decision rules
Prediction. What is Prediction. Simple methods for Prediction. Classification by decision tree induction. Classification and regression evaluation
Prediction Prediction What is Prediction Simple methods for Prediction Classification by decision tree induction Classification and regression evaluation 2 Prediction Goal: to predict the value of a given
Lecture 6: Unsupervised Machine Learning Dagmar Gromann International Center For Computational Logic
SEMANTIC COMPUTING Lecture 6: Unsupervised Machine Learning Dagmar Gromann International Center For Computational Logic TU Dresden, 23 November 2018 Overview Unsupervised Machine Learning overview Association
Data Mining. Practical Machine Learning Tools and Techniques. Slides for Chapter 3 of Data Mining by I. H. Witten, E. Frank and M. A.
Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 3 of Data Mining by I. H. Witten, E. Frank and M. A. Hall Output: Knowledge representation Tables Linear models Trees Rules
Unsupervised: no target value to predict
Clustering Unsupervised: no target value to predict Differences between models/algorithms: Exclusive vs. overlapping Deterministic vs. probabilistic Hierarchical vs. flat Incremental vs. batch learning
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
Data Mining Part 5. Prediction
Data Mining Part 5. Prediction 5.4. Spring 2010 Instructor: Dr. Masoud Yaghini Outline Using IF-THEN Rules for Classification Rule Extraction from a Decision Tree 1R Algorithm Sequential Covering Algorithms
Outline. RainForest A Framework for Fast Decision Tree Construction of Large Datasets. Introduction. Introduction. Introduction (cont d)
Outline RainForest A Framework for Fast Decision Tree Construction of Large Datasets resented by: ov. 25, 2004 1. 2. roblem Definition 3. 4. Family of Algorithms 5. 6. 2 Classification is an important
Association Rules. Charles Sutton Data Mining and Exploration Spring Based on slides by Chris Williams and Amos Storkey. Thursday, 8 March 12
Association Rules Charles Sutton Data Mining and Exploration Spring 2012 Based on slides by Chris Williams and Amos Storkey The Goal Find patterns : local regularities that occur more often than you would
Data Mining Concepts & Techniques
Data Mining Concepts & Techniques Lecture No. 03 Data Processing, Data Mining Naeem Ahmed Email: naeemmahoto@gmail.com Department of Software Engineering Mehran Univeristy of Engineering and Technology
Jue Wang (Joyce) Department of Computer Science, University of Massachusetts, Boston Feb Outline
Learn to Use Weka Jue Wang (Joyce) Department of Computer Science, University of Massachusetts, Boston Feb-09-2010 Outline Introduction of Weka Explorer Filter Classify Cluster Experimenter KnowledgeFlow
Data Mining. 3.2 Decision Tree Classifier. Fall Instructor: Dr. Masoud Yaghini. Chapter 5: Decision Tree Classifier
Data Mining 3.2 Decision Tree Classifier Fall 2008 Instructor: Dr. Masoud Yaghini Outline Introduction Basic Algorithm for Decision Tree Induction Attribute Selection Measures Information Gain Gain Ratio
Data Mining Practical Machine Learning Tools and Techniques
Decision trees Extending previous approach: Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 6 of Data Mining by I. H. Witten and E. Frank to permit numeric s: straightforward
Summary. Machine Learning: Introduction. Marcin Sydow
Outline of this Lecture Data Motivation for Data Mining and Learning Idea of Learning Decision Table: Cases and Attributes Supervised and Unsupervised Learning Classication and Regression Examples Data:
Classification. Instructor: Wei Ding
Classification Decision Tree Instructor: Wei Ding Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Preliminaries Each data record is characterized by a tuple (x, y), where x is the attribute
Naïve Bayes Classification. Material borrowed from Jonathan Huang and I. H. Witten s and E. Frank s Data Mining and Jeremy Wyatt and others
Naïve Bayes Classification Material borrowed from Jonathan Huang and I. H. Witten s and E. Frank s Data Mining and Jeremy Wyatt and others Things We d Like to Do Spam Classification Given an email, predict
Data Mining. 3.3 Rule-Based Classification. Fall Instructor: Dr. Masoud Yaghini. Rule-Based Classification
Data Mining 3.3 Fall 2008 Instructor: Dr. Masoud Yaghini Outline Using IF-THEN Rules for Classification Rules With Exceptions Rule Extraction from a Decision Tree 1R Algorithm Sequential Covering Algorithms
Naïve Bayes Classification. Material borrowed from Jonathan Huang and I. H. Witten s and E. Frank s Data Mining and Jeremy Wyatt and others
Naïve Bayes Classification Material borrowed from Jonathan Huang and I. H. Witten s and E. Frank s Data Mining and Jeremy Wyatt and others Things We d Like to Do Spam Classification Given an email, predict
ARTIFICIAL INTELLIGENCE (CS 370D)
Princess Nora University Faculty of Computer & Information Systems ARTIFICIAL INTELLIGENCE (CS 370D) (CHAPTER-18) LEARNING FROM EXAMPLES DECISION TREES Outline 1- Introduction 2- know your data 3- Classification
CSE4334/5334 DATA MINING
CSE4334/5334 DATA MINING Lecture 4: Classification (1) CSE4334/5334 Data Mining, Fall 2014 Department of Computer Science and Engineering, University of Texas at Arlington Chengkai Li (Slides courtesy
Horizontal Aggregation in SQL to Prepare Dataset for Generation of Decision Tree using C4.5 Algorithm in WEKA
Horizontal Aggregation in SQL to Prepare Dataset for Generation of Decision Tree using C4.5 Algorithm in WEKA Mayur N. Agrawal 1, Ankush M. Mahajan 2, C.D. Badgujar 3, Hemant P. Mande 4, Gireesh Dixit
9/6/14. Our first learning algorithm. Comp 135 Introduction to Machine Learning and Data Mining. knn Algorithm. knn Algorithm (simple form)
Comp 135 Introduction to Machine Learning and Data Mining Our first learning algorithm How would you classify the next example? Fall 2014 Professor: Roni Khardon Computer Science Tufts University o o o
Tillämpad Artificiell Intelligens Applied Artificial Intelligence Tentamen , , MA:8. 1 Search (JM): 11 points
Lunds Tekniska Högskola EDA132 Institutionen för datavetenskap VT 2017 Tillämpad Artificiell Intelligens Applied Artificial Intelligence Tentamen 2016 03 15, 14.00 19.00, MA:8 You can give your answers
Sponsored by AIAT.or.th and KINDML, SIIT
CC: BY NC ND Table of Contents Chapter 2. Data Preprocessing... 31 2.1. Basic Representation for Data: Database Viewpoint... 31 2.2. Data Preprocessing in the Database Point of View... 33 2.3. Data Cleaning...
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
Part I. Instructor: Wei Ding
Classification Part I Instructor: Wei Ding Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Classification: Definition Given a collection of records (training set ) Each record contains a set
Data Mining. 2.4 Data Integration. Fall Instructor: Dr. Masoud Yaghini. Data Integration
Data Mining 2.4 Fall 2008 Instructor: Dr. Masoud Yaghini Data integration: Combines data from multiple databases into a coherent store Denormalization tables (often done to improve performance by avoiding
Data Mining Algorithms: Basic Methods
Algorithms: The basic methods Inferring rudimentary rules Data Mining Algorithms: Basic Methods Chapter 4 of Data Mining Statistical modeling Constructing decision trees Constructing rules Association
Homework 1 Sample Solution
Homework 1 Sample Solution 1. Iris: All attributes of iris are numeric, therefore ID3 of weka cannt be applied to this data set. Contact-lenses: tear-prod-rate = reduced: none tear-prod-rate = normal astigmatism
BITS F464: MACHINE LEARNING
BITS F464: MACHINE LEARNING Lecture-16: Decision Tree (contd.) + Random Forest Dr. Kamlesh Tiwari Assistant Professor Department of Computer Science and Information Systems Engineering, BITS Pilani, Rajasthan-333031
COMP33111: Tutorial and lab exercise 7
COMP33111: Tutorial and lab exercise 7 Guide answers for Part 1: Understanding clustering 1. Explain the main differences between classification and clustering. main differences should include being unsupervised
Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4. Introduction to Data Mining
Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar (modified by Predrag Radivojac, 2017) Classification:
Clustering Analysis Basics
Clustering Analysis Basics Ke Chen Reading: [Ch. 7, EA], [5., KPM] Outline Introduction Data Types and Representations Distance Measures Major Clustering Methodologies Summary Introduction Cluster: A collection/group
Decision Trees: Discussion
Decision Trees: Discussion Machine Learning Spring 2018 The slides are mainly from Vivek Srikumar 1 This lecture: Learning Decision Trees 1. Representation: What are decision trees? 2. Algorithm: Learning
Nominal Data. May not have a numerical representation Distance measures might not make sense. PR and ANN
NonMetric Data Nominal Data So far we consider patterns to be represented by feature vectors of real or integer values Easy to come up with a distance (similarity) measure by using a variety of mathematical
CS34800, Fall 2016, Assignment 4
1 CS34800, Fall 2016, Assignment 4 Due 11:59pm 07 December (Wed.), 2016 * if you submit it by Tuesday, Dec. 06 then it will be graded and returned back to you after lecture on Friday, Dec. 09. If you submit
Data Mining and Machine Learning. Instance-Based Learning. Rote Learning k Nearest-Neighbor Classification. IBL and Rule Learning
Data Mining and Machine Learning Instance-Based Learning Rote Learning k Nearest-Neighbor Classification Prediction, Weighted Prediction choosing k feature weighting (RELIEF) instance weighting (PEBLS)
Data Mining. Part 2. Data Understanding and Preparation. 2.4 Data Transformation. Spring Instructor: Dr. Masoud Yaghini. Data Transformation
Data Mining Part 2. Data Understanding and Preparation 2.4 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Introduction Normalization Attribute Construction Aggregation Attribute Subset Selection Discretization
Machine Learning in Real World: C4.5
Machine Learning in Real World: C4.5 Industrial-strength algorithms For an algorithm to be useful in a wide range of realworld applications it must: Permit numeric attributes with adaptive discretization
CMPUT 391 Database Management Systems. Data Mining. Textbook: Chapter (without 17.10)
CMPUT 391 Database Management Systems Data Mining Textbook: Chapter 17.7-17.11 (without 17.10) University of Alberta 1 Overview Motivation KDD and Data Mining Association Rules Clustering Classification
1. make a scenario and build a bayesian network + conditional probability table! use only nominal variable!
Project 1 140313 1. make a scenario and build a bayesian network + conditional probability table! use only nominal variable! network.txt @attribute play {yes, no}!!! @graph! play -> outlook! play -> temperature!
Classification: Basic Concepts, Decision Trees, and Model Evaluation
Classification: Basic Concepts, Decision Trees, and Model Evaluation Data Warehousing and Mining Lecture 4 by Hossen Asiful Mustafa Classification: Definition Given a collection of records (training set
DESIGN AND IMPLEMENTATION OF BUILDING DECISION TREE USING C4.5 ALGORITHM
1 Proceedings of SEAMS-GMU Conference 2007 DESIGN AND IMPLEMENTATION OF BUILDING DECISION TREE USING C4.5 ALGORITHM KUSRINI Abstract. Decision tree is one of data mining techniques that is applied in classification
Data Mining. SPSS Clementine k-means Algorithm. Spring 2010 Instructor: Dr. Masoud Yaghini. Clementine
Data Mining SPSS 12.0 6. k-means Algorithm Spring 2010 Instructor: Dr. Masoud Yaghini Outline K-Means Algorithm in K-Means Node References K-Means Algorithm in Overview The k-means method is a clustering
Data Mining and Analytics
Data Mining and Analytics Aik Choon Tan, Ph.D. Associate Professor of Bioinformatics Division of Medical Oncology Department of Medicine aikchoon.tan@ucdenver.edu 9/22/2017 http://tanlab.ucdenver.edu/labhomepage/teaching/bsbt6111/
An Empirical Study on feature selection for Data Classification
An Empirical Study on feature selection for Data Classification S.Rajarajeswari 1, K.Somasundaram 2 Department of Computer Science, M.S.Ramaiah Institute of Technology, Bangalore, India 1 Department of
Classification with Decision Tree Induction
Classification with Decision Tree Induction This algorithm makes Classification Decision for a test sample with the help of tree like structure (Similar to Binary Tree OR k-ary tree) Nodes in the tree
IMPACT MODELS AND DATA MATTEO DE FELICE
IMPACT MODELS AND DATA MATTEO DE FELICE What is an impact model What is an impact model What is an impact model What is an impact model Modelling the influence of something on something else What is an
Nominal Data. May not have a numerical representation Distance measures might not make sense PR, ANN, & ML
Decision Trees Nominal Data So far we consider patterns to be represented by feature vectors of real or integer values Easy to come up with a distance (similarity) measure by using a variety of mathematical
Data Mining and Machine Learning: Techniques and Algorithms
Instance based classification Data Mining and Machine Learning: Techniques and Algorithms Eneldo Loza Mencía eneldo@ke.tu-darmstadt.de Knowledge Engineering Group, TU Darmstadt International Week 2019,
Analytical model A structure and process for analyzing a dataset. For example, a decision tree is a model for the classification of a dataset.
Glossary of data mining terms: Accuracy Accuracy is an important factor in assessing the success of data mining. When applied to data, accuracy refers to the rate of correct values in the data. When applied
Data Analytics and Boolean Algebras
Data Analytics and Boolean Algebras Hans van Thiel November 28, 2012 c Muitovar 2012 KvK Amsterdam 34350608 Passeerdersstraat 76 1016 XZ Amsterdam The Netherlands T: + 31 20 6247137 E: hthiel@muitovar.com
What Is Data Mining? CMPT 354: Database I -- Data Mining 2
Data Mining What Is Data Mining? Mining data mining knowledge Data mining is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data CMPT
Extra readings beyond the lecture slides are important:
1 Notes To preview next lecture: Check the lecture notes, if slides are not available: http://web.cse.ohio-state.edu/~sun.397/courses/au2017/cse5243-new.html Check UIUC course on the same topic. All their
MAT 155. Chapter 1 Introduction to Statistics. sample. population. parameter. statistic
MAT 155 Dr. Claude Moore Cape Fear Community College Chapter 1 Introduction to Statistics 1 1Review and Preview 1 2Statistical Thinking 1 3Types of Data 1 4Critical Thinking 1 5Collecting Sample Data Key
Sabbatical Leave Report
Zdravko Markov, Ph.D. Phone: (860) 832-2711 Associate Professor of Computer Science E-mail: markovz@ccsu.edu Central Connecticut State University URL: http://www.cs.ccsu.edu/~markov/ Sabbatical Leave Report
Classification and Regression
Classification and Regression Announcements Study guide for exam is on the LMS Sample exam will be posted by Monday Reminder that phase 3 oral presentations are being held next week during workshops Plan
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
Data Mining Classification - Part 1 -
Data Mining Classification - Part 1 - Universität Mannheim Bizer: Data Mining I FSS2019 (Version: 20.2.2018) Slide 1 Outline 1. What is Classification? 2. K-Nearest-Neighbors 3. Decision Trees 4. Model
Data Mining. SPSS Clementine Apriori Algorithm. Fall 2009 Instructor: Dr. Masoud Yaghini. Clementine
Data Mining SPSS 12.0 6. Apriori Algorithm Fall 2009 Instructor: Dr. Masoud Yaghini Outline Overview Apriori Node Example of Apriori Node: Market Basket Analysis References Overview Overview Association
Data Mining 4. Cluster Analysis
Data Mining 4. Cluster Analysis 4.5 Spring 2010 Instructor: Dr. Masoud Yaghini Introduction DBSCAN Algorithm OPTICS Algorithm DENCLUE Algorithm References Outline Introduction Introduction Density-based
International Journal of Computer Techniques Volume 4 Issue 4, July August 2017
RESEARCH ARTICLE OPEN ACCESS Enhanced ification Model for Cervical Cancer Dataset based on Cost Sensitive ifier Hayder K. Fatlawi Information Technology Research and Development Center, University of Kufa,
Homework # 4. Example: Age in years. Answer: Discrete, quantitative, ratio. a) Year that an event happened, e.g., 1917, 1950, 2000.
Homework # 4 1. Attribute Types Classify the following attributes as binary, discrete, or continuous. Further classify the attributes as qualitative (nominal or ordinal) or quantitative (interval or ratio).
Advanced learning algorithms
Advanced learning algorithms Extending decision trees; Extraction of good classification rules; Support vector machines; Weighted instance-based learning; Design of Model Tree Clustering Association Mining
Attribute Discretization and Selection. Clustering. NIKOLA MILIKIĆ UROŠ KRČADINAC
Attribute Discretization and Selection Clustering NIKOLA MILIKIĆ nikola.milikic@fon.bg.ac.rs UROŠ KRČADINAC uros@krcadinac.com Naive Bayes Features Intended primarily for the work with nominal attributes
Unit 10 Databases. Computer Concepts Unit Contents. 10 Operational and Analytical Databases. 10 Section A: Database Basics
Unit 10 Databases Computer Concepts 2016 ENHANCED EDITION 10 Unit Contents Section A: Database Basics Section B: Database Tools Section C: Database Design Section D: SQL Section E: Big Data Unit 10: Databases
Data Preparation. Data Preparation. (Data pre-processing) Why Prepare Data? Why Prepare Data? Some data preparation is needed for all mining tools
Data Preparation Data Preparation (Data pre-processing) Why prepare the data? Discretization Data cleaning Data integration and transformation Data reduction, Feature selection 2 Why Prepare Data? Why
Part 1. The Review of Linear Programming The Revised Simplex Method
In the name of God Part 1. The Review of Linear Programming 1.4. Spring 2010 Instructor: Dr. Masoud Yaghini Introduction Outline in Tableau Format Comparison Between the Simplex and the Revised Simplex
MACHINE LEARNING Example: Google search
MACHINE LEARNING Lauri Ilison, PhD Data Scientist 20.11.2014 Example: Google search 1 27.11.14 Facebook: 350 million photo uploads every day The dream is to build full knowledge of the world and know everything
CONCEPT FORMATION AND DECISION TREE INDUCTION USING THE GENETIC PROGRAMMING PARADIGM
1 CONCEPT FORMATION AND DECISION TREE INDUCTION USING THE GENETIC PROGRAMMING PARADIGM John R. Koza Computer Science Department Stanford University Stanford, California 94305 USA E-MAIL: Koza@Sunburn.Stanford.Edu
DATA ANALYSIS WITH WEKA. Author: Nagamani Mutteni Asst.Professor MERI
DATA ANALYSIS WITH WEKA Author: Nagamani Mutteni Asst.Professor MERI Topic: Data Analysis with Weka Course Duration: 2 Months Objective: Everybody talks about Data Mining and Big Data nowadays. Weka is
DATA MINING LAB MANUAL
DATA MINING LAB MANUAL Subtasks : 1. List all the categorical (or nominal) attributes and the real-valued attributes seperately. Attributes:- 1. checking_status 2. duration 3. credit history 4. purpose
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
CAP-359 PRINCIPLES AND APPLICATIONS OF DATA MINING. Rafael Santos
CAP-359 PRINCIPLES AND APPLICATIONS OF DATA MINING Rafael Santos rafael.santos@inpe.br www.lac.inpe.br/~rafael.santos/ Overview So far What is Data Mining? Applications, Examples. Let s think about your
Introduction to Machine Learning
Introduction to Machine Learning Decision Tree Example Three variables: Attribute 1: Hair = {blond, dark} Attribute 2: Height = {tall, short} Class: Country = {Gromland, Polvia} CS4375 --- Fall 2018 a
SOCIAL MEDIA MINING. Data Mining Essentials
SOCIAL MEDIA MINING Data Mining Essentials Dear instructors/users of these slides: Please feel free to include these slides in your own material, or modify them as you see fit. If you decide to incorporate
Chapter 3: Data Mining:
Chapter 3: Data Mining: 3.1 What is Data Mining? Data Mining is the process of automatically discovering useful information in large repository. Why do we need Data mining? Conventional database systems
Data Mining. Lesson 9 Support Vector Machines. MSc in Computer Science University of New York Tirana Assoc. Prof. Dr.
Data Mining Lesson 9 Support Vector Machines MSc in Computer Science University of New York Tirana Assoc. Prof. Dr. Marenglen Biba Data Mining: Content Introduction to data mining and machine learning
Machine Learning Techniques for Data Mining
Machine Learning Techniques for Data Mining Eibe Frank University of Waikato New Zealand 10/25/2000 1 PART VII Moving on: Engineering the input and output 10/25/2000 2 Applying a learner is not all Already
Spatial Patterns Point Pattern Analysis Geographic Patterns in Areal Data
Spatial Patterns We will examine methods that are used to analyze patterns in two sorts of spatial data: Point Pattern Analysis - These methods concern themselves with the location information associated
2. (a) Briefly discuss the forms of Data preprocessing with neat diagram. (b) Explain about concept hierarchy generation for categorical data.
Code No: M0502/R05 Set No. 1 1. (a) Explain data mining as a step in the process of knowledge discovery. (b) Differentiate operational database systems and data warehousing. [8+8] 2. (a) Briefly discuss
An Introduction to WEKA Explorer. In part from: Yizhou Sun 2008
An Introduction to WEKA Explorer In part from: Yizhou Sun 2008 What is WEKA? Waikato Environment for Knowledge Analysis It s a data mining/machine learning tool developed by Department of Computer Science,,
CS 206 Introduction to Computer Science II
CS 206 Introduction to Computer Science II 04 / 06 / 2018 Instructor: Michael Eckmann Today s Topics Questions? Comments? Graphs Definition Terminology two ways to represent edges in implementation traversals
Multi-label classification using rule-based classifier systems
Multi-label classification using rule-based classifier systems Shabnam Nazmi (PhD candidate) Department of electrical and computer engineering North Carolina A&T state university Advisor: Dr. A. Homaifar
IENG484 Quality Engineering Lab 1 RESEARCH ASSISTANT SHADI BOLOUKIFAR
IENG484 Quality Engineering Lab 1 RESEARCH ASSISTANT SHADI BOLOUKIFAR SPSS (Statistical package for social science) Originally is acronym of Statistical Package for the Social Science but, now it stands
Machine Learning. Decision Trees. Le Song /15-781, Spring Lecture 6, September 6, 2012 Based on slides from Eric Xing, CMU
Machine Learning 10-701/15-781, Spring 2008 Decision Trees Le Song Lecture 6, September 6, 2012 Based on slides from Eric Xing, CMU Reading: Chap. 1.6, CB & Chap 3, TM Learning non-linear functions f: