Intelligent Databases and Information Systems Department of Computer Science and Artificial Intelligence, University of Granada, Spain © Fernando Berzal,

Slides:

Advertisements

Similar presentations

A Data Mining Course for Computer Science and non Computer Science Students Jamil Saquer Computer Science Department Missouri State University Springfield,

Advertisements

Florida International University COP 4770 Introduction of Weka.

Data Mining Techniques: Classification. Classification What is Classification? –Classifying tuples in a database –In training set E each tuple consists.

IT 433 Data Warehousing and Data Mining

Decision Tree Approach in Data Mining

Bab /44 Bab 4 Classification: Basic Concepts, Decision Trees & Model Evaluation Part 1 Classification With Decision tree.

1 Data Mining Classification Techniques: Decision Trees (BUSINESS INTELLIGENCE) Slides prepared by Elizabeth Anglo, DISCS ADMU.

By Dan Stalloch. Association – what could be linked together in away with something Patterns – sequential and time series, shows us how often certain.

Building an Intelligent Web: Theory and Practice Pawan Lingras Saint Mary’s University Rajendra Akerkar American University of Armenia and SIBER, India.

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

Spatial and Temporal Data Mining V. Megalooikonomou Introduction to Decision Trees ( based on notes by Jiawei Han and Micheline Kamber and on notes by.

Continuous Data Stream Processing

Department of Computer Science, University of Waikato, New Zealand Eibe Frank WEKA: A Machine Learning Toolkit The Explorer Classification and Regression.

Data Mining with Decision Trees Lutz Hamel Dept. of Computer Science and Statistics University of Rhode Island.

Rule induction: Ross Quinlan's ID3 algorithm Fredda Weinberg CIS 718X Fall 2005 Professor Kopec Assignment #3.

Extraction of high-level features from scientific data sets Eui-Hong (Sam) Han Department of Computer Science and Engineering University of Minnesota Research.

Clementine Server Clementine Server A data mining software for business solution.

CSC 466: Knowledge Discovery From Data Alex Dekhtyar Department of Computer Science Cal Poly New Computer Science Elective.

Copyright © 2004 Pearson Education, Inc.. Chapter 27 Data Mining Concepts.

Introduction to WEKA Aaron 2/13/2009. Contents Introduction to weka Download and install weka Basic use of weka Weka API Survey.

© Prentice Hall1 DATA MINING Introductory and Advanced Topics Part II Margaret H. Dunham Department of Computer Science and Engineering Southern Methodist.

Presented To: Madam Nadia Gul Presented By: Bi Bi Mariam.

Oracle Data Mining Ying Zhang. Agenda Data Mining Data Mining Algorithms Oracle DM Demo.

Presenter: Teng-Chih Yang Professor: Ming-Puu Chen Date: 10/ 28/ 2009 Data mining in course management systems: Moodle case study and tutorial Romero,

Introduction to Data Mining Engineering Group in ACL.

OLAM and Data Mining: Concepts and Techniques. Introduction Data explosion problem: –Automated data collection tools and mature database technology lead.

Contributed by Yizhou Sun 2008 An Introduction to WEKA.

Kansas State University Department of Computing and Information Sciences CIS 830: Advanced Topics in Artificial Intelligence From Data Mining To Knowledge.

Data Mining: Classification

Last Words COSC Big Data (frameworks and environments to analyze big datasets) has become a hot topic; it is a mixture of data analysis, data mining,

1 Research Groups : KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems SCI 2 SMetrology and Models Intelligent.

Introduction to Data Mining Group Members: Karim C. El-Khazen Pascal Suria Lin Gui Philsou Lee Xiaoting Niu.

Decision Trees & the Iterative Dichotomiser 3 (ID3) Algorithm David Ramos CS 157B, Section 1 May 4, 2006.

Algorithms: The Basic Methods Witten – Chapter 4 Charles Tappert Professor of Computer Science School of CSIS, Pace University.

Computational Intelligence: Methods and Applications Lecture 19 Pruning of decision trees Włodzisław Duch Dept. of Informatics, UMK Google: W Duch.

Machine Learning Lecture 1. Course Information Text book “Introduction to Machine Learning” by Ethem Alpaydin, MIT Press. Reference book “Data Mining.

Comparing Univariate and Multivariate Decision Trees Olcay Taner Yıldız Ethem Alpaydın Department of Computer Engineering Bogazici University

Data Mining By Dave Maung.

Department of Computer Science, University of Waikato, New Zealand Eibe Frank WEKA: A Machine Learning Toolkit The Explorer Classification and Regression.

Multi-Relational Data Mining: An Introduction Joe Paulowskey.

CS690L Data Mining: Classification

27-18 września Data Mining dr Iwona Schab. 2 Semester timetable ORGANIZATIONAL ISSUES, INDTRODUCTION TO DATA MINING 1 Sources of data in business,

Advanced Analytics on Hadoop Spring 2014 WPI, Mohamed Eltabakh 1.

Data Mining and Decision Trees 1.Data Mining and Biological Information 2.Data Mining and Machine Learning Techniques 3.Decision trees and C5 4.Applications.

Summary „Data mining” Vietnam national university in Hanoi, College of technology, Feb.2006.

An Introduction Student Name: Riaz Ahmad Program: MSIT( ) Subject: Data warehouse & Data Mining.

Summary „Rough sets and Data mining” Vietnam national university in Hanoi, College of technology, Feb.2006.

Classification using Decision Trees 1.Data Mining and Information 2.Data Mining and Machine Learning Techniques 3.Decision trees and C5 4.Applications.

1 Classification: predicts categorical class labels (discrete or nominal) classifies data (constructs a model) based on the training set and the values.

Data Mining By Farzana Forhad CS 157B. Agenda Decision Tree and ID3 Rough Set Theory Clustering.

Cluster Analysis Data Mining Experiment Department of Computer Science Shenzhen Graduate School Harbin Institute of Technology.

DECISION TREES Asher Moody, CS 157B. Overview  Definition  Motivation  Algorithms  ID3  Example  Entropy  Information Gain  Applications  Conclusion.

Eick: kNN kNN: A Non-parametric Classification and Prediction Technique Goals of this set of transparencies: 1.Introduce kNN---a popular non-parameric.

WHAT IS DATA MINING?  The process of automatically extracting useful information from large amounts of data.  Uses traditional data analysis techniques.

Chapter 3 Data Mining: Classification & Association Chapter 4 in the text box Section: 4.3 (4.3.1),

DATA MINING TECHNIQUES (DECISION TREES ) Presented by: Shweta Ghate MIT College OF Engineering.

Department of Computer Science Sir Syed University of Engineering & Technology, Karachi-Pakistan. Presentation Title: DATA MINING Submitted By.

Organization and Knowledge Management

Prepared by: Mahmoud Rafeek Al-Farra

RESEARCH APPROACH.

ALZHEIMER DISEASE PREDICTION USING DATA MINING TECHNIQUES P.SUGANYA (RESEARCH SCHOLAR) DEPARTMENT OF COMPUTER SCIENCE TIRUPPUR KUMARAN COLLEGE FOR WOMEN.

SEEM5770/ECLT5840 Course Review

Waikato Environment for Knowledge Analysis

What is Pattern Recognition?

Research Areas Christoph F. Eick

Welcome! Knowledge Discovery and Data Mining

Decision Tree  Decision tree is a popular classifier.

Decision Tree  Decision tree is a popular classifier.

Presentation transcript:

Intelligent Databases and Information Systems Department of Computer Science and Artificial Intelligence, University of Granada, Spain © Fernando Berzal, 2001

1 Introducing TMiner... Integrated Data Mining framework written in Java. JDBC gives access to virtually any relational database in the market.

2 Introducing TMiner... Knowledge workers can analyze their own data using a stardard WIMP interface

3 And also through the web running TMiner as an applet... Introducing TMiner...

4 Data Mining techniques Association rule mining (Apriori & TBAR) Association rule mining (Apriori & TBAR) Classification models Classification models –Top-Down Induction of Decision Trees –ART (Association Rule Trees) –STAR Methodology (AQ & CN2) –Parametric & Non-parametric classifiers e.g. Euclidean & Quadratic classifiers, k-NN, LVQ, DSM... Clustering algorithms Clustering algorithms e.g. K-Means, GRASP clustering, ISODATA...

5 Association rule mining “TBAR: efficient method for association rule mining in relational databases” Fernando Berzal, Juan Carlos Cubero, Nicolás Marín & José María Serrano Data & Knowledge Engineering, 37 (2001), 47-64

6 Classification: TDIDT Decision Tree Inducer based on Quinlan’s C4.5  Alternative partition rules Entropy Gain ratio Gini index MaxDif  ‘else’ branches  Multi-way decision trees for continuous attributes

7 Classification: ART

8 Numerical Cruncher Pattern Recognition Algorithm Collection

9 Software download TMiner Personal Edition is available from