Possibilities for Applying Data Mining for Early Warning in Food Supply Networks Adrie J.M. Beulens,Yuan Li, Mark R. Kramer, Jack G.A.J. van der Vorst.

Slides:

Advertisements

Similar presentations

Applications of one-class classification

Advertisements

Bayesian Network and Influence Diagram A Guide to Construction And Analysis.

Intelligent Environments1 Computer Science and Engineering University of Texas at Arlington.

Back-propagation Chih-yun Lin 5/16/2015. Agenda Perceptron vs. back-propagation network Network structure Learning rule Why a hidden layer? An example:

LEARNING FROM OBSERVATIONS Yılmaz KILIÇASLAN. Definition Learning takes place as the agent observes its interactions with the world and its own decision-making.

Radu Calin Dimitriu Modelling the Hot-Strength of Creep-Resistant Ferritic Steels and Relationship to Creep-Rupture.

CSE803 Fall Pattern Recognition Concepts Chapter 4: Shapiro and Stockman How should objects be represented? Algorithms for recognition/matching.

Learning From Data Chichang Jou Tamkang University.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-1 Chapter 4 Modeling and Analysis Turban,

Modeling Gene Interactions in Disease CS 686 Bioinformatics.

Building Knowledge-Driven DSS and Mining Data

Introduction. 1.Data Mining and Knowledge Discovery 2.Data Mining Methods 3.Supervised Learning 4.Unsupervised Learning 5.Other Learning Paradigms 6.Introduction.

Stockman CSE803 Fall Pattern Recognition Concepts Chapter 4: Shapiro and Stockman How should objects be represented? Algorithms for recognition/matching.

Neural Networks in Data Mining “An Overview”

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

Decision analysis and Risk Management course in Kuopio

GUHA method in Data Mining Esko Turunen Tampere University of Technology Tampere, Finland.

Data Mining By Andrie Suherman. Agenda Introduction Major Elements Steps/ Processes Tools used for data mining Advantages and Disadvantages.

CSCI 347 / CS 4206: Data Mining Module 01: Introduction Topic 03: Stages in Data Mining.

Data Mining Techniques

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

Data Mining Joyeeta Dutta-Moscato July 10, Wherever we have large amounts of data, we have the need for building systems capable of learning information.

Soft Computing Lecture 20 Review of HIS Combined Numerical and Linguistic Knowledge Representation and Its Application to Medical Diagnosis.

Chapter 13 Genetic Algorithms. 2 Data Mining Techniques So Far… Chapter 5 – Statistics Chapter 6 – Decision Trees Chapter 7 – Neural Networks Chapter.

COMP3503 Intro to Inductive Modeling

Using Neural Networks in Database Mining Tino Jimenez CS157B MW 9-10:15 February 19, 2009.

PhD Committee J. Vanthienen (promotor, K.U.Leuven) J. Vandenbulcke

1 Pattern Recognition Concepts How should objects be represented? Algorithms for recognition/matching * nearest neighbors * decision tree * decision functions.

Modeling and Analysis By Dr.S.Sridhar,Ph.D., RACI(Paris),RZFM(Germany),RMR(USA),RIEEEProc. web-site :

MBA7025_01.ppt/Jan 13, 2015/Page 1 Georgia State University - Confidential MBA 7025 Statistical Business Analysis Introduction - Why Business Analysis.

MGS3100_01.ppt/Aug 25, 2015/Page 1 Georgia State University - Confidential MGS 3100 Business Analysis Introduction - Why Business Analysis Aug 25 and 26,

1. 2 Traditional Income Statement LO1: Prepare a contribution margin income statement.

1 Pattern Recognition Pattern recognition is: 1. A research area in which patterns in data are found, recognized, discovered, …whatever. 2. A catchall.

MBA7020_01.ppt/June 13, 2005/Page 1 Georgia State University - Confidential MBA 7020 Business Analysis Foundations Introduction - Why Business Analysis.

Data Mining In contrast to the traditional (reactive) DSS tools, the data mining premise is proactive. Data mining tools automatically search the data.

CAPSLOG: 'Capitalizing on collaboration in sustainable logistics in food and flower chains’ Prof. dr. Jack G.A.J. van der Vorst Prof. dr. Wout Dullaert.

Handwritten digit recognition

Data Mining BY JEMINI ISLAM. Data Mining Outline: What is data mining? Why use data mining? How does data mining work The process of data mining Tools.

Operations Research The OR Process. What is OR? It is a Process It assists Decision Makers It has a set of Tools It is applicable in many Situations.

October 2-3, 2015, İSTANBUL Boğaziçi University Prof.Dr. M.Erdal Balaban Istanbul University Faculty of Business Administration Avcılar, Istanbul - TURKEY.

Neural Networks Demystified by Louise Francis Francis Analytics and Actuarial Data Mining, Inc.

Data Mining and Decision Support

Chong Ho Yu.  Data mining (DM) is a cluster of techniques, including decision trees, artificial neural networks, and clustering, which has been employed.

Data Mining By: Johan Johansson. Mining Techniques Association Rules Association Rules Decision Trees Decision Trees Clustering Clustering Nearest Neighbor.

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

Using Bayesian Belief Networks in Assessing Software Architectures Jilles van Gurp & Jan Bosch.

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

SUPERVISED AND UNSUPERVISED LEARNING Presentation by Ege Saygıner CENG 784.

Managing Qualitative Knowledge in Software Architecture Assesment Jilles van Gurp & Jan Bosch Högskolan Karlskrona/Ronneby in Sweden Department of Software.

How do you get here?

Linking Threats to Assets in Complex Ecological and Socio-Economic Systems: Qualitative Modelling for Tourism Development in North Western Australia Jeffrey.

Machine Learning Usman Roshan Dept. of Computer Science NJIT.

Data Mining is the process of analyzing data and summarizing it into useful information Data Mining is usually used for extremely large sets of data It.

DATA MINING and VISUALIZATION Instructor: Dr. Matthew Iklé, Adams State University Remote Instructor: Dr. Hong Liu, Embry-Riddle Aeronautical University.

Prepared by Fayes Salma.  Introduction: Financial Tasks  Data Mining process  Methods in Financial Data mining o Neural Network o Decision Tree  Trading.

Lecture on Bayesian Belief Networks (Basics)

Prepared by: Mahmoud Rafeek Al-Farra

RESEARCH APPROACH.

Data Mining Techniques So Far…

Prediction as Data Mining Task

Prepared by: Mahmoud Rafeek Al-Farra

Prepared by: Mahmoud Rafeek Al-Farra

The Good News: Why are Decision Trees Currently Quite Popular for Classification Problems? Very robust --- good average testing performance: outperform.

Pattern Recognition & Machine Learning

A task of induction to find patterns

Scientific forecasting

A task of induction to find patterns

Chapter 14 February 26, 2004.

The Good News: Why are Decision Trees Currently Quite Popular for Classification Problems? Very robust --- good average testing performance: outperform.

Introduction to Decision Sciences

Presentation transcript:

Possibilities for Applying Data Mining for Early Warning in Food Supply Networks Adrie J.M. Beulens,Yuan Li, Mark R. Kramer, Jack G.A.J. van der Vorst

Agenda Framework Requirements in relation to data mining Case study

Food supply networks inputs Output s Complexity, variability, uncertainty… Quantification of performance indicators

Information Systems in FSN Transparency, traceability, tractability E-commerce Scanning Total quality management HACCP …

Framework for Early Warning

Requirements on Data mining About processes Problem detection Finding determinant factors Prediction About presentation Complex structure representation Different representation forms New knowledge incorporation

Contribution of Data mining - I Requirements imposed by early warning system Function of data mining 1.Predict2.Detect problem 3.Find determinant factors 4.Describe complex structure Deviation detectionValid Factor selection *Helpful ClassificationValidHelpful RegressionValidHelpful Dependence modelValidHelpfulValid Causal modelValid * Factor selection methods are usually regarded as a pre-processing step for data mining rather than a separate data mining function.

Contribution of Data mining - II DM methods DM Function Decision trees Neural networks Bayesian networks Association rules Nearest neighbor Deviation detection Valid ClassificationValid RegressionValid Dependence model Valid Causal modelValid

Contribution of Data mining - III Data mining methodRepresentation formNovel knowledge incorporation Decision trees Easy Association rulesRulesEasy Neural networksLinear or Nonlinear model Difficult Nearest neighborsExample-base methodsDifficult Bayesian networksProbabilistic graphical dependency model Easy

Other remarks on technique selection Data format Quantitative Qualitative How well the model class is able to represent patterns in data sets Heuristic expert rules Meta-learning landmarking

Case study Stages of the chicken supply chain: One of the Performance Indicators: percentage of dead chicken upon arrival Deviation: chicken’s Death-On-Arrival (DOA) HatcheryBroiler Farm Transport Slaughter house

Case study – Result with Decision trees

Questions? © Wageningen UR