WEKA's Knowledge Flow Interface Data Mining Knowledge Discovery in Databases ELIE TCHEIMEGNI Department of Computer Science Bowie State University, MD.

Slides:



Advertisements
Similar presentations
Florida International University COP 4770 Introduction of Weka.
Advertisements

Weka & Rapid Miner Tutorial By Chibuike Muoh. WEKA:: Introduction A collection of open source ML algorithms – pre-processing – classifiers – clustering.
Weka. Preprocessing Opening a file Editing a file Visualize a variable.
Exploratory Data Mining and Data Preparation
WEKA Evaluation of WEKA Waikato Environment for Knowledge Analysis Presented By: Manoj Wartikar & Sameer Sagade.
Department of Computer Science, University of Waikato, New Zealand Eibe Frank WEKA: A Machine Learning Toolkit The Explorer Classification and Regression.
March 25, 2004Columbia University1 Machine Learning with Weka Lokesh S. Shrestha.
Neural Networks Chapter Feed-Forward Neural Networks.
An Extended Introduction to WEKA. Data Mining Process.
Data Mining: A Closer Look Chapter Data Mining Strategies (p35) Moh!
Introduction to WEKA Aaron 2/13/2009. Contents Introduction to weka Download and install weka Basic use of weka Weka API Survey.
1 Statistical Learning Introduction to Weka Michel Galley Artificial Intelligence class November 2, 2006.
Jump to first page The objective of our final project is to evaluate several supervised learning algorithms for identifying pre-defined classes among web.
Chapter 5 Data mining : A Closer Look.
5 Copyright © 2009, Oracle. All rights reserved. Defining ETL Mappings for Staging Data.
1 © Goharian & Grossman 2003 Introduction to Data Mining (CS 422) Fall 2010.
An Exercise in Machine Learning
 The Weka The Weka is an well known bird of New Zealand..  W(aikato) E(nvironment) for K(nowlegde) A(nalysis)  Developed by the University of Waikato.
Overview DM for Business Intelligence.
Rapid Miner Session CIS 600 Analytical Data Mining,EECS, SU Three steps for use  Assign the dataset file first  Select functionality  Execute.
WEKA – Knowledge Flow & Simple CLI
WEKA - Explorer (sumber: WEKA Explorer user Guide for Version 3-5-5)
WEKA and Machine Learning Algorithms. Algorithm Types Classification (supervised) Given -> A set of classified examples “instances” Produce -> A way of.
Appendix: The WEKA Data Mining Software
The Knowledge Flow Interface 김개원 데이터베이스 연구실. 1. Overview.
Chapter 9 Neural Network.
1 1 Slide Evaluation. 2 2 n Interactive decision tree construction Load segmentchallenge.arff; look at dataset Load segmentchallenge.arff; look at dataset.
Weka: a useful tool in data mining and machine learning Team 5 Noha Elsherbiny, Huijun Xiong, and Bhanu Peddi.
Hands-on predictive models and machine learning for software Foutse Khomh, Queen’s University Segla Kpodjedo, École Polytechnique de Montreal PASED - Canadian.
1 Running Clustering Algorithm in Weka Presented by Rachsuda Jiamthapthaksin Computer Science Department University of Houston.
Department of Computer Science, University of Waikato, New Zealand Eibe Frank WEKA: A Machine Learning Toolkit The Explorer Classification and Regression.
Constructing Data Mining Applications based on Web Services Composition Ali Shaikh Ali and Omer Rana
Machine Learning with Weka Cornelia Caragea Thanks to Eibe Frank for some of the slides.
Weka: Experimenter and Knowledge Flow interfaces Neil Mac Parthaláin
1 1 Slide Using Weka. 2 2 Slide Data Mining Using Weka n What’s Data Mining? We are overwhelmed with data We are overwhelmed with data Data mining is.
1Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall. Exploring Microsoft Office Excel 2010 by Robert Grauer, Keith Mulbery, and Mary.
1Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall. Exploring Microsoft Office Excel 2010 by Robert Grauer, Keith Mulbery, and Mary.
TEXT ANALYTICS - LABS Maha Althobaiti Udo Kruschwitz Massimo Poesio.
Chapter 11 Statistical Techniques. Data Warehouse and Data Mining Chapter 11 2 Chapter Objectives  Understand when linear regression is an appropriate.
An Investigation of Commercial Data Mining Presented by Emily Davis Supervisor: John Ebden.
Weka – A Machine Learning Toolkit October 2, 2008 Keum-Sung Hwang.
CSE/CIS 787 Analytical Data Mining, Dept. of EECS, SU Three steps for use  Assign the dataset file first  Assign the analysis type you want.
Introduction to Weka Xingquan (Hill) Zhu Slides copied from Jeffrey Junfeng Pan (UST)
W E K A Waikato Environment for Knowledge Aquisition.
An Exercise in Machine Learning
FCM WIZARD IN ACTION SCENARIO: HIV DRUG RESISTANCE PREDICTION Main developers: Gonzalo NÁPOLES and Isel GRAU Supervisors: Elpiniki PAPAGEORGIOU and Koen.
***Classification Model*** Hosam Al-Samarraie, PhD. CITM-USM.
Weka Overview Sagar Samtani and Hsinchun Chen Spring 2016, MIS 496A
Copyright  2004 limsoon wong Using WEKA for Classification (without feature selection)
Machine Learning in Practice Lecture 2 Carolyn Penstein Rosé Language Technologies Institute/ Human-Computer Interaction Institute.
In part from: Yizhou Sun 2008 An Introduction to WEKA Explorer.
A Decision Support Based on Data Mining in e-Banking Irina Ionita Liviu Ionita Department of Informatics University Petroleum-Gas of Ploiesti.
@relation age sex { female, chest_pain_type { typ_angina, asympt, non_anginal,
WEKA: A Practical Machine Learning Tool WEKA ： A Practical Machine Learning Tool.
Detecting Web Attacks Using Multi-Stage Log Analysis
Machine Learning with Spark MLlib
Waikato Environment for Knowledge Analysis
WEKA.
Sampath Jayarathna Cal Poly Pomona
Weka Package Weka package is open source data mining software written in Java. Weka can be applied to your dataset from the GUI, the command line or called.
Machine Learning with Weka
Adapted from: Prof. Pedro Larrañaga Technical University of Madrid
Opening Weka Select Weka from Start Menu Select Explorer Fall 2003
CSCI N317 Computation for Scientific Applications Unit Weka
Machine Learning with WEKA
Lecture 10 – Introduction to Weka
Dimensionality Reduction
Assignment 1: Classification by K Nearest Neighbors (KNN) technique
Data Mining CSCI 307, Spring 2019 Lecture 7
Data Mining CSCI 307, Spring 2019 Lecture 8
Presentation transcript:

WEKA's Knowledge Flow Interface Data Mining Knowledge Discovery in Databases ELIE TCHEIMEGNI Department of Computer Science Bowie State University, MD

DOWNLOAD WEKA a/files/weka-3-6-windows-jre- x64/3.6.8/weka-3-6-8jre- x64.exe/download?use_mirror=hivel ocity a/files/weka-3-6-windows-jre- x64/3.6.8/weka-3-6-8jre- x64.exe/download?use_mirror=hivel ocity

Overview ● Knowledge Flow Interface - WEKA components are selected from a tool bar, positioned a layout canvas, and connected into a directed graph to model a complete system that processes and analyzes data

The Knowledge Flow Environment

Data Sources ● used to indicate where data is coming from ● supports various file types and sources ● configurable for - file name of data source - dataset or instance (incremental) loading

Data Sinks ● used to indicate where data is going to be written ● supports various file types and sources ● configurable for - file name of data source

Filters ● used to preprocess data prior to classification or learning ● supports both supervised and unsupervised filters ● configurable depending on filter type

Classifiers ● supports all classification algorithms presented in the textbook ● parameters are configurable depending on classification algorithm

Clusterers ● supports all clustering algorithms presented in the textbook ● parameters are configurable depending on clustering algorithm

Evaluation ● used to configure both inputs to and outputs from algorithms ● supports various algorithm performance evaluators ● output format fairly "standardized"

Visualization ● used to visually display outputs ● supports performance and summaries ● comparable to options from Explorer interface

Example: Decision Tree Classifier 1) Specify a data source - select ARFFloader - right­click the ARFFloader icon to configure by specifying the location of the dataset

Example: Decision Tree Classifier 2) Specify which attribute is the class - select ClassAssigner from the Evaluation panel - right­click the ARFFloader icon and select dataset to connect to ClassAssigner - right­click the ClassAssigner to configure the target class

Example: Decision Tree Classifier 3) Specify cross­validation - select CrossValidation FoldMaker from the Evaluation panel - right­click the ClassAssigner icon and select dataset to connect to CVFoldMaker - right­click CrossValidation FoldMaker to configure...

Example: Decision Tree Classifier 4) Specify decision tree algorithm - select J48 from the Classifiers panel - right­click the CVFoldMaker icon and select trainingSet to connect to J48; repeat, but select testSet to connect to J48

Example: Decision Tree Classifier 5) Specify evaluation - select ClassifierPerformance Evaluator from the Evaluation panel - right­click the J48 icon and select batchClassifier to connect to ClassifierPerformance Evaluator

Example: Decision Tree Classifier 6) Specify evaluation output - select TextViewer from the Visualization panel - right­click the ClassifierPerformance Evaluator icon and select text to connect to TextViewer

Example: Decision Tree Classifier 7) To allow viewing of decision trees per fold - select GraphViewer from the Visualization panel - right­click the J48 icon and select graph to connect to GraphViewer

Example: Decision Tree Classifier 8) Run experiments - right­click ARFFloader icon and select Start loading - right­click TextViewer icon for textual summary - right­click GraphViewer icon to view decision tree for each fold - reconfigure and re­run...

Example: Incremental Learning ● Classifiers: - AODE, Bayes averaged, one­dependence estimators - NaiveBayesUpdateable, incremental naïve Bayes classifier that learns one instance at a time - Winnow, mistake­driven perceptron with multiplicative updates - instance­based learners: IB1, IBk, KStar, LWL

Example: Incremental Learning ● Filters: Add AddExpression Copy FirstOrder MakeIndicator MergeTwoValues NonSparseToSparse NumericToBinary NumericTransform Obfuscate Remove RemoveType RemoveWithValues SparseToNonSparse SwapValues

Example: Incremental Learning ● Configure an experiment based on the figure to the right... Note: Although incremental learning algorithms can process data files that are too large to fit in memory, many instance­based learners store entire dataset internally.

Example: Incremental Learning ● Strip charts - scrolling data plot showing both the accuracy and the root mean­squared probability error against time

Feedback: Privacy Policy Feedback
Do Not Sell My Personal Information
About project: SlidePlayer Terms of Service

© 2025 SlidePlayer.com. Inc. All rights reserved.