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التنقيب في البيانات

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  • General

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    course face book group site link
    www.facebook.com/groups/591723705166919/
    Lectures Videos
    https://drive.google.com/file/d/1zl1J2AzPlTPi9HaX4xy1m3yP5dDV3Wm7/view?usp=sharing
    https://drive.google.com/file/d/1s9RQydCtsrJQy1VzVIjOef8Q_8Nj9dJm/view?usp=sharing
    https://drive.google.com/file/d/1Holt2_eMl0oxGg7ZArfINcIg-vXFLE3b/view?usp=sharing
    https://drive.google.com/file/d/1utTo6k5w-YsQkq0y0zLK4ZCjANrdU6F-/view?usp=sharing
    https://drive.google.com/file/d/1ApT-iXVPRiN5kmlOb6hWQcllSU2JdPLQ/view?usp=sharing
    https://drive.google.com/file/d/1PHHdSNdIuXgltr-YDvWqvwV_d24_Z_WR/view?usp=sharing
    https://drive.google.com/file/d/1LOSHIts-cAZlyj6SzSxOm1TsiEjcMh54/view?usp=sharing
    https://drive.google.com/file/d/1C8F9ssPHMOK9Xw7lZwF_kQj73nph_slN/view?usp=sharing
    https://drive.google.com/file/d/1ezKyxkbuooCndRutXZw3TlqejG2Bcn5s/view?usp=sharing
    https://drive.google.com/file/d/1HxwbUf1oI-QWrq8a55TZMYWWtXQuBSBq/view?usp=sharing
    https://drive.google.com/file/d/1LMHudaM5PnPONF6rg_WuGRGU0T_3t6pP/view?usp=sharing
    https://drive.google.com/file/d/18AZWNimc52wqv-lTfsfIH_ri41VXLhgH/view?usp=sharing
    https://drive.google.com/file/d/1rIK00OPMtzizdq6UGDd44tEMMBrGM-OG/view?usp=sharing
    https://drive.google.com/file/d/1jffWELp12NX_BHU7Jr_Pjvd9gcaQ5Hco/view?usp=sharing
    https://drive.google.com/file/d/11-4aKQeHxsR7Gn2B44w0KHEcwP22j-OW/view?usp=sharing
    https://drive.google.com/file/d/1_VFphxtqku9a0FL7H9NSCzEs9hlArE9W/view?usp=sharing
    https://drive.google.com/file/d/1ZzuLwf2ZprSwfqvDMImroPHJrshWbLIO/view?usp=sharing
    https://drive.google.com/file/d/1fVrU6z1-MuQ9zUWB8DXH9SzdTYD4yEYo/view?usp=sharing
    https://drive.google.com/file/d/1RKBaOTi43fIX6FNdD5iIOEdwa-4i0L_7/view?usp=sharing
    https://drive.google.com/file/d/1eOL5Y0CWSTwNyjP3fbc8FEDQ420k4WAW/view?usp=sharing
    https://drive.google.com/file/d/1a_1kzDNkyBNxvA8ISulAtdhyywM_b214/view?usp=sharing
    https://drive.google.com/file/d/1-lSkL5bDL6w5F7-D2TEKnTYZqi4URPpN/view?usp=sharing
    https://drive.google.com/file/d/13dUsS9at8eNBg2WtThjlPTmVCuz2Y1l_/view?usp=sharing
    https://drive.google.com/file/d/1LVxfSZukR_8X6w9EkmKzjMNVF9XYKfL8/view?usp=sharing
    https://drive.google.com/file/d/1FaQ4Qkofyioe3bNPShIPIW8i6J0aGrn4/view?usp=sharing
    https://drive.google.com/file/d/1NeXqtBQOXLCw4HriIhlrYrETGr4qOQ1d/view?usp=sharing
    https://drive.google.com/file/d/1kRlhkakGuGLozPiZVMrmdH-TOTXfMb-n/view?usp=sharing
    https://drive.google.com/file/d/1X6DWt1VgqszXZb5Gu3psPZacBKd4LBjR/view?usp=sharing

    • An Najah National University

      Faculty of Engineering and Information Technology

      CIS Department


      Course title :  Data Mining 
      Instructor(s) name(s) : Dr. Fady DRaidi

      Compulsory / Elective:Elective

      Prerequisites:-------

      _________________________________________

      Course Description:

      Data Mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and forecasting, and generally improve their performance through interaction with data. It is currently regarded as the key element of a more general process called Knowledge Discovery that deals with extracting useful knowledge from raw data. The knowledge discovery process includes data selection, cleaning, coding, using different statistical and machine learning techniques, and visualization of the generated structures. The course will cover all these issues and will illustrate the whole process by examples

       
      Course Objectives :
      • Present fundamental concepts and techniques for data mining
      • Provide necessary background for applying data mining to business problems
      • Conduct case studies on real data mining examples
      • Practice data mining tools on real data 
       
      Intended learning Outcomes and Competences

      Upon successful completion of the course the student will:

      • Be able to understand the concepts, strategies, and methodologies related to the design and construction of data mining
      • Be able to comprehend several data preprocessing methods
      • Be able to utilize data warehouses and OLAP for data mining and knowledge discovery activities
      • Be able to determine an appropriate mining strategy for given large dataset
      • Be able to apply appropriate mining techniques to extract unexpected patterns and new rules that are "hidden" in large databases
      • Be able to obtain knowledge of current data mining applications

  • Introduction to Data Mining

    chapter 1: Introduction to Data Mining

  • Data Preproccssing

    chapter 2: Data
    • Chapter Objectives:
      • Discuss the types, attributes, measurements and quality of data.
      • Present data preprocessing i.e., aggregation, sampling, etc.
      • Talk about the measures of similarity and dissimilarity between data objects

      Intended learning Outcomes, upon completion the student:
      • Able to distinguish between data objects and objects attributes
      • Understand the various types of types and their values
      • Be able to comprehend several data preprocessing methods
      • Be able to comprehend several similarity and dissimilarity methods between data objects

       

      Study Plan:
      First:
      Students should read the following attached tutorial slides for meeting the mentioned above objectives
      , then solve the assignment and visit these URLs
    • resource icon
      File
      02 DataPrec 2 File
    • resource icon
      File
      02 DataPrec 1 File
    • assign icon
      Assignment
      Assignment One
    • resource icon
      File
      Data File

  • Classification

    chapter 3: Classification

  • Association Rules Analysis

    chapter 4: Association Analysis
    • Association Analysis

       

      Chapter Objectives
      • Present the basic concept and algorithms of association rules
      • Present items set generation using the priori principle
      • Present the generation of candidate and pruning
      • Explain how rules are generated
      • Show how association models can be evaluated

       

      Intended learning Outcomes, upon completion the student:
      • be able to understand and distinguish an association problems
      • be able to use algorithm that reduce the complexity of time required for rules generation
      • be able to test and evaluate the association models

      First:
      Students should read the following attached tutorial slides for meeting the mentioned above objectives
    • resource icon
      File
      association analysis File

  • Cluster Analysis

    chapter 5: Cluster Analysis: Basic Concepts and Algorithms
    • Chapter Objectives
      • Present the basic concept and algorithms of clustering
      • Present the k-mean clustering algorithm
      • Show how clustering algorithms can be evaluated
    • Intended learning Outcomes, upon completion the student:
      • be able to understand and distinguish clustering problems
      • be able to use k-mean clustering algorithm
      • be able to test and evaluate the clustering algorithm
      First:
      Students should read the following attached tutorial slides for meeting the mentioned above objectives
    • resource icon
      File
      cluster analysis-part1 File
    • resource icon
      File
      Final-DM2013 File
    • resource icon
      File
      Final-DM2014 File

  • Topic 6

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  • Topic 20

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