ITCS 6162 Knowledge Discovery in Databases - Spring 2022 ( Sec 001 ) - ONLINE

Instructor: Dr. Angelina A Tzacheva, Department of Computer Science, College of Computing and Informatics,
Office: 435A Woodward, Office Hours: Tuesday 3:00pm - 5:00pm Email:

Join Zoom Meeting

SkypeID:    angelina.tzacheva

Teaching Assistants:

1. Nikhitha Modugu,  Email:
OfficeHours: Wednesday 10:00 AM - 11:30 PM & Thursday 10:00AM-11:30PM via 
  Zoom links :

Wednesday 10:00 AM - 11:30 PM


Thursday 10:00AM-11:30PM


SkypeID: live:.cid.fe97b230ceb66342

2. Kamalapriya Srinivasan,  Email:

OfficeHours: Monday 12:00PM-1:30PM & Tuesday 12:00PM-1:30PM via 
  Zoom links :

Monday 12:00PM-1:30PM


Tuesday 12:00PM-1:30PM


SkypeID: live:.cid.c20e8d32014e341f

3. Avyay Yennamaneni,  Email:

OfficeHours: Tuesday 11:30AM-1PM & Friday 10:30AM-12PM via 
  Zoom links :

Tuesday 11:30AM-1PM


OfficeHoursDay2: Friday 10:30AM-12PM



Prerequisites: ITCS 6160 Database Systems

"Introduction to Data Mining (2nd Edition)" by Pang-Ning Tan, Michael Steinbauch, Anuj Karpatne and Vipin Kumar, 2018. ISBN-10: 0133128903

Course Outline:
- Knowledge discovery process
- Types of Data, Pre-processing, Distance Measures
- Association rules discovery methods
- Action rules discovery
- Discretization algorithms
- Decision Trees
- Classification methods
- Clustering Analysis
- Hadoop, MapReduce, and distributed data mining
- Application is specific domain (health, financial, education, music)

Student Learning Outcomes:
1. Demonstrate Knowledge of Data Mining Techniques, including Granularity Based Approaches, for Incomplete Data
2. Actively Manage and Participate in Data Mining Projects
3. Extract Actionable Knowledge from Data and use it build Recommender Systems

Instructional Method: 
This is an Online course which includes Video Lectures, Reading Assignments, Exercises, Group Activities, and a Group Project.
Lectures Notes, Videos, and Reading Assignments are posted in the syllabus table below, as well as on Canvas. Please download and read each lecture material and view each Video on the specified day.
All material by date is listed, including preparation for the exams with sample questions. The Exams are open-book / open-notes. The textbook is necessary, as exam questions are based on lecture notes AND on the text, and Exercises are assigned from the textbook.

Credit Hours: This is a 3-credit hour course.
This course is designed to require about 10 hours per week - for readings, exams, exercises, video cases, and group project work.
The material is technical and requires dedication of time to comprehend.  To complete course successfully, Please do not plan on   cramming all lectures the day before the exam. Designate 3 hours every lecture day for reading the given lecture, and book chapter. Designate additional 4 hours per week for Exercises, videocase assignments, and Group meetings / activities. You can meet with your Group Members ONLINE through video conferencing - via Skype, Google Hangout, or meet in person if desired. Students are expected to communicate and meet with their group members to complete the project successfully.
Exercises are assigned after each chapter. The Exercises are due on Canvas on the dates they are assigned. Exercises are *not accepted* through e-mail. Late Exercises are not accepted.

The final course grade is determined on the following weights:
Exercises   20%
Group Activities 15%
Midterm Exam   17%
Group Project   20%
Final Exam   18%
Video Cases 12%

Grading scale:
A   90% - 100%
B   80% - 89%
C   70% - 79%
D   60% - 69%
F   less than 60%
X   academic dishonesty

Academic Integrity and Honesty:
Students are required to read and abide by the Code of Student Academic Integrity available from Dean of Students Office. This code forbids cheating, fabrication or falsification of information, multiple submissions of academic work, plagiarism (including viewing others work without instructor permission), abuse of academic materials, and complicity of academic dishonesty. Violations of the Code of Student Academic Integrity, including plagiarism, result in disciplinary action as provided by the Code.

We are concerned with a positive learning experience. This course strives to create an inclusive academic climate in which the dignity of all individuals is respected and maintained. We value diversity that is beneficial to both employers and society at large. Students are encouraged to actively and appropriately share their views in class discussions.

Inclement Weather:
University Policy Statement #13 states the University is open unless the Chancellor announces that the University is closed. In the event of inclement weather, check your e-mail, and Canvas. The instructor will post a message on Canvas, and through e-mail. The instructor will use their best judgment as to whether class should be held.

UNC Charlotte is committed to access to education. If you have a disability and need academic accommodations, please provide a letter of accommodation from Disability Services early in the semester. For more information on accommodations, contact the Office of Disability Services.

The University policy on Course Withdrawal allows students a limited number of opportunities available to withdraw from courses. There are financial and academic consequences that may result from course withdrawal. If a student is concerned about his / her ability to succeed in this course, it is important to make an appointment to speak with the instructor as soon as possible.

Syllabus Revision:
The instructor may modify the class schedule and syllabus during the course of the semester. For example - additional educational videos may be posted. Same changed will appear on Canvas. Students are responsible for refreshing their syllabus once per week.

E-Mail Communication:
Students are responsible for *all* announcements made in class and on the class online resources. Students should check the online class resources throughout the semester. The Instructor and Teaching Assistants send occasional e-mails with important information. We send this information to the student's university e-mail address listed on Banner system.

Class Expectation:
By attending class beyond the first week, students agree to follow the framework and rules related to this course as described above.




Jan 12

Preview of course syllabus      |     Find your Group - members here   for the  Group Project


Overview of Knowledge Discovery in Databases (KDD) - I

Read Chapter 1 from the book today.
Exercise:    2.   Chapter 1      //to turn in:  save solution in a text file and upload  to Canvas 
Overview of KDD (continued) - II

VideoCase_01 : Knowledge Discovery in Databases KDD Process

video: L01_01KDDDefinition
video: L01_02DataInformationKnowledge
video: L01_03KDDProcess
video: L01_04KDDContributingAreas

Jan 19

Data - Types, Quality, Pre-processing, Similarity Measures_E2 | Data - Types, Quality, Pre-processing, Similarity Measures
Read Chapter 2 from the book today.
Exercises:    2.   and    14.   Chapter 2

VideoCase 02 : DataTypes_Similarity_Measures

video: L02_01WhatIsData_TypesOfAttributes
video: L02_02TypesOfAttributes_Outliers
video: L02_03PlottingOfObjects_CurseOfDimensionality
video: L02_04SamplingFeatureSelection_DistanceEculidean

Group_01 Moderator

Mathematical Background Review - Intro To Set Theory

Association Rule Mining - Agrawal (Apriori) method (frequent item-sets)        |        Alternate Slides

Read Chapter 6 from the book today.

Exercise:   2. (a) (b)    Chapter 6


PowerPoint: L03_04AssociationRuleMining
PowerPoint: L03_05AssociationRuleMining_SupportConfidence

PowerPoint: L03_06AssociationRuleMining_MarketBasketAnalysis

PowerPoint: L03_07AssociationRuleMining_AlgorithmSteps

PowerPoint: L03_08MarketBasketAnalysis_Algorithms

PowerPoint: L03_09MarketBasketAnalysis_Example

video: L03_01IntroToSetTheorySetsElementsEmtpySetUniversalSet
video: L03_02IntroToSetTheoryIntersectionUnionComplementSetDifference

video: L03_03AprioriAlgorithm_02        |          video: L03_03AssociationRulesIntroAprioriAgarwalMethod

video: L03_04AssociationRuleMining

video: L03_06AssociationRuleMining_MarketBasketAnalysis

video: L03_07AssociationRuleMining_AlgorithmSteps

video: L03_08MarketBasketAnalysis_Algorithms

video: L03  10AssociationRuleMining_BusinessStrategies

PlayCode:_01_L03_AssociationRuleMining   |   video:L03_AssociationRuleMiningCodeDemo_01


Group_07 Moderator

Similarity Measures using Vectors
Read Chapter 6 from the book today.

PowerPoint: L20_01Introduction

PowerPoint: L20_02CosineSimilarity
PowerPoint: L20_03JaccardSimilarityIndex

PowerPoint: L20_04CorrelationSimilarity

PowerPoint: L20_05EuclideanDistance

PowerPoint: L20_06ComparisonOfSimilarityMeasures

PowerPoint: L20_07SimilarityMeasures

PowerPoint: L20_08RealTimeApplication


video: L20_02CosineSimilarity
video: L20_03JaccardSimilarityIndex
video: L20_04CorrelationSimilarity
video: L20_05EuclideanDistance

video: L20_06ComparisonOfSimilarityMeasures

video: L20_07SimilarityMeasures

video: L20_08RealTimeApplications

PlayCode:_07_L20_SimilarityMeasures   |  video:_L20SimilarityMeasures_CodeDemo

Exercise 19 Chapter 2  // this exercise is optional, and it is for ExtraCredit. Submit ONLY if you missed one exercise before.

Jan 26

Argawal (Apriori) method (frequent item-sets) Example
Exercise:    6.   Chapter 6

VideoCase 03 : Apriori_Algorithm

video: L04_01SupportAndConfidence_AssociationRules
video: L04_02AprioriEample_FrequentItemsets
video: L04_03AprioriExample_AssociationRules

Group_03 Moderator

Frequent Pattern Growth Strategy (FP-Tree)
      |   Alternate Slides
Exercise:  build the FP-Tree  using the transactions from table 6.24 (in exercise 8  chapter 6)

PowerPoint: L05_07FPGrowthTree_Strategy_02       |        PowerPoint: L05_07FPGrowthTree_Strategy

PowerPoint: L05_08FPGrowthTree_Example

PowerPoint: L05_09FPGrowthTree_Parameters

PowerPoint: L05_10FPGrowthTree_History

PowerPoint: L05_11FPGrowthTree_AdvantagesDisadvantages

PowerPoint: L05_12FPGrowthTree_Approaches

PowerPoint: L05_13FPGrowthTree_Mining

PowerPoint: L05_14FPGrowthTree_Properties

video: L05_01FrequentPatternTree_FPTree01
video: L05_02FPTree02
video: L05_03FPTree03
video: L05_04MiningTheFPTree01
video: L05_05MiningTheFPTree02

video: L05_06FPGrowthTree_Applications

video: L05_07FPGrowthTree_Strategy_02       |          video: L05_07FPGrowthTree_Strategy

video: L05_08FPGrowthTree_Example

video: L05_09FPGrowthTree_Parameters

video: L05_10FPGrowthTree_History

video: L05_11FPGrowthTree_AdvantagesDisadvantages

video: L05_12FPFGrowthTree_Approaches

video: L05_13FPGrowthTree_Mining

video: L05_14FPGrowthTree_Properties

PlayCode:_03_L05_FPGrowthTree   |   video:L05_FPGrowthTree_CodeDemo_01


Feb 02

Group_04 Moderator

Decision - LERS (certain and possible rules)       |         Alternate Slides

Powerpoint: L06_01LERSIntroduction_02

Powerpoint: L06_06LERS_Examples

PowerPoint: L06_07LERS_ProblemSolving

PowerPoint: L06_08LERS_Objects

PowerPoint: L06_09LERS_RoughSetTheory

PowerPoint: L06_10LERS_RSES

PowerPoint: L06_11LERS_DataAnalysisMethodsInRSES_02        |      PowerPoint: L06_11LERS_DataAnalysisMethodsInRSES_01

video: L06_01LERSIntroduction_02        |           video:L06_01LERSIntroduction

video: L06_02LERSExampleFirstLoop
video: L06 03LERSExampleCertainPossibleRules
video: L06 04LERSExampleSecondLoop
video: L06 05LERSExampleThirdLoopEnd

video: L06_06LERS_Examples

video: L06_07LERS_Objects

video: L06_08LERS_ProblemSolving

video: L06_09LERS_RoughSetTheory

video: L06_10LERS_RSES

video: L06_11DataAnalysisMethods_RSES_02    |     video: L06_11LERS_DataAnalysisMethods_RSES_01

PlayCode: 04_L06_LERS   |   video: L06_LERS_CodeDemo_01

                                            video: L06_LERS_CodeDemo_02

Exercise:     download LERS software - calculate rules using data from the lecture above
// to turn in:   take a screen shot of your runtime environment showing the rules | upload the screen shot to Canvas

Exercise8.Chapter6. (ExtraCreditOnly)   // this exercise is Optional and it is for ExtraCredit . Submit ONLY if you missed one exercise before 

Group_06 Moderator

Action Rules Intro           |           Action Rules 1         |          Action Rules 2

PowerPoint: L07_01ActionRules_Introduction_02      |     PowerPoint: L07_01ActionRules_Introducion_01

PowerPoint: L07_02ActionRules_Examples

PowerPoint: L07_03ActionRules_MethodExtraction

PowerPoint: L07_04ActionRules_Validation

PowerPoint: L07_05ActionRulesImplementation

PowerPoint: L07_06ActionRules_Summary

PowerPoint: L07_07ActionRules_Steps

PowerPoint: L07_08ActionRules_Attributes

video: L07_01ActionRules_Introduction_02      |     video: L07_01ActionRules_Introducion_01

video: L07_02ActionRules_Examples

video: L07_03ActionRules_MethodExtraction

video: L07_04ActionRules_Validation

video: L07_05ActionRulesImplementation

video: L07_06ActionRules_Summary

video: L07_07ActionRules_Steps

video: L07_08ActionRules_Attributes

PlayCode: 06_L07_ActionRules   |   video: L07ActionRules_CodeDemo

Action Rule Discovery Example 

VideoCase 04 : Introduction_to_Action_Rules

video: L07  01ActionRulesIntroduction
video: L07_02ActionRulesIntroSupportConfidence
video: L07_03ActionRulesExample


Example project :

ActionRulesJavaExample2     |    Demo Video 1: DemoActionRulesJava    Demo Video 2: DemoActionRulesJava2

Feb 09

Rough Sets

VideoCase 05 : Decision_Rules

Preparing for Midterm Exam

video: L08_01SetTheory_MidtermPreparing
video: L08_02AprioriFrequentItemsets1_MidtermPreparing
video: L08_03AprioriFrequentItemsets2_MidtermPreparing
video: L08_04AprioriAssociationRules
video: L08_05FPTree_MidtermPreparing
video: L08_06LERS MidtermPreparing
video: L08_07ActionRules_MidtermPreparing

Feb 16

Midterm Exam
- access exam on Canvas
- complete the exam according to the time given on Canvas (8 pm to 11 pm) 

Feb 23

Group_02 Moderator

Decision Trees - Discovery System ID3        |           Decision Tree, Random Forest


Read Chapter 4 from the book today.
Exercise:    2.   Chapter 4

PowerPoint: L09_01DecisionTreesIntroduction_02

PowerPoint: L09_02DecisionTreesIntroExamples_02

PowerPoint: L09_05DecisionTree_AdvantagesAndDisadvantages

PowerPoint: L09_06DecisionTree_History

PowerPoint: L09_07DecisionTree_Usecase

PowerPoint: L09_08DecisionTree_AttributeSelection

PowerPoint: L09_09DecisionTree_Comparison

video: L09_01DecisionTreesIntroduction_02      |     video: L09_01DecisionTreesIntroduction_01

video: L09_02DecisionTreesIntroExamples_02   |    video: L09_02DecisionTreesIntroExamples_01
video: L09_03DecisionTreeEntropyInformationGain

video: L09_04DecisionTree_RandomForest

video: L09_05DecisionTree_AdvantagesAndDisadvantages

video: L09_06DecisionTree_History

video: L09_07DecisionTree_Usecase

video: L09_08DecisionTree_AttributeSelection'

video: L09_09DecisionTree_Comparison

PlayCode: 02_L09_DecisionTree        |           video: L09_DecisionTree_CodeDemo_01

                                                                    video: L09_DecisionTree_CodeDemo_02

System ID3 Example          |         Mathematical Background Review - Logarithm


Exercise:   3.    Chapter 4

VideoCase 06 : Decision_Trees

video: L10_01System_ID3_Example_Entropy
video: L10_02System_ID3_Example_Entropy02
video: L10_03System_ID3_Example_AtributeSelection

Decision Tree Example Code - Windows Version         |         Decision  Tree Example Code - Linux Version



video: Exercise_02_DecisionTree_Program_Code_DecisionTree_Java_DEMO

video: L12_04_DecisionTreeExampleCode_DEMO      |      video:L12_04_DecisionTree_ExampleCode_DEMO_02      |      video:L12_04_DecisionTree_ExampleCode_DEMO_03      |      video:L12_04_DecisionTree_Ex

Mar 02

Discovery System Rosetta

video: L11_01DiscoverySystemRosetta_Example
video: L11_02DiscoverySystemRosetta_DiscernibilityMatrix
video: L11_03DiscoverySystemRosetta_DiscernibilityFunction

Mining Incomplete Data

VideoCase 07 : Missing_Data_Mining


GroupActivity_01 :   Download RSES Software | Calculate Rules and Classify Data
// one group member submits this Exercise for the whole group
// to turn in : save your .rses project file ( File | Save As in RSES ) and upload the .rses file  to Canvas

Mar 09

Spring Recess - No Classes

Mar 16         

Distributed Knowledge Systems, Distributed Query Answering, and Semantics

Distributed Query Answering Example

VideoCase 08 : Distributed_Query-Answering

video: L12_01DistributedQueryAnsweringExamplePart1
video: L12_02DistributedQueryAnsweringExamplePart2

Mar 23

Distributed Data Mining - Hadoop , HDFS , MapReduce , HIVE   |   Cloud Tools Overview   |  Basic HDFS Commands

VideoCase 11 : Cloud_Tools_Overview_01_Introduction_to_Hive(Extra Credit)

video: L02_01_Hadoop_DistributedFileSystem
video: L02_02_HDFS_NameNode_DataNode
video: L02_03_HDFS_Pipelining_Rebalancer_UI
video: L02_04_HDFS_UserInterfaceCommands_BasicFeatures
video: L02_05_HDFS_FSNamespace_Replication
video: L02_06_HDFS_Protocol_Failure_Integrity
video: L02_07_HDFS_Staging_Pipelining_Interface

video: L19 01 Hadoop DistributedFileSystem
video: L19 02 HDFS Architecture NameNode DataNode Pipelining
video: L19 03 HDFS Rebalancer UserInterface BasicCommands
video: L19 04 MapReduce DataFlow Features
video: L19 05 MapReduce WordCountCode Partitioners Combiners Compression Counters
video: L19 06 MapReduce SpeculativeExecution ZeroReducers DistributedFileCache

Mar 30

Cloud Tools Overview Continued:  Pig , Hive , HBase , Storm

Pig     |     Hive     |     HBase     |    Zookeeper

VideoCase 12 : Cloud_Tools_Overview_02_Introduction_to_Pig(Extra Credit)

video: L04 01 Pig   
video: L04 02 Hive  
video: L04 03 HBase 
video: L04 04 Storm

video : ActionRulesMR-RandomForestExample

MR-RandomForestExample_01    |     MR-RandomForestExample_02     |     MR-AprioriExample     |     MR-OntologyExample


video: Project_Demo_ActionRuleExtraction


Project Assignment - files due
//to turn in: upload PowerPoint file, Video File, and Source Code to  Canvas

Apr 06

Group_08 Moderator

Discretization Example RSES

VideoCase 09 : Data_Discretization

GroupActivity_04 :  using RSES software | open a dataset | discretize the dataset
// to turn in : save your .rses project file ( File | Save As in RSES ) and upload the .rses file to Canvas
// one group member submits this Exercise for the whole group

PowerPoint: L13_01DiscretizationIntroduction_02

PowerPoint: L13_07StepsInDataDiscretization

PowerPoint: L13_08DataDiscretization_Mapping

PowerPoint: L13_09DataDiscretization_KMeans

PowerPoint: L13_10DataDiscretization_ClusterAnalysis

PowerPoint: L13_11DiscernibilityAlgorithm

PowerPoint: L13_12DecisionTreeAnalysis

PowerPoint: L13_13BinningDiscretization

video: L13 01DiscretizationIntroduction_02     |     video: L13_01DiscretizationIntroduction_01
video: L13 02DiscretizatinonQuantization
video: L13 03RSESAlgorithmOptimalSetOfCuts
video: L13 04DiscretizationExampleRSESPart1
video: L13 05DiscretizationExampleRSESPart2
video: L13 06DiscretizationExampleRSESPart3

video: L13 07StepsInDataDescretization
video: L13 08DataDiscretization_Mapping
video: L13 09DataDiscretization_KMeans
video: L13 10DataDiscretization_ClusterAnalysis
video: L13 11DiscernibilityAlgorithm 

video: L13 12DecisionTreeAnalysis
video: L13 13BinningDiscretization
video: L13 14DataDiscretization_CodeDemo_02    |     video: L13_14DataDiscretization_CodeDemo_01

PlayCode: 08_L13_DataDiscretization       |           video: L13_DataDiscretization_CodeDemo_01

Apr 13

Group_10 Moderator

Cluster Analysis -Basic Concepts and Algorithms_E2 | Cluster Analysis - Basic Concepts and Algorithms

Read Chapter 8 from the book today.

PowerPoint: L14_02_ClusterAnalysisIntroPlottingOfObjects_02

PowerPoint: L14_03_ClusterAnalysisPreProcessingOfData_02

PowerPoint: L14_05_PartitioningClusteringKMeans_02

PowerPoint: L14_07_KMeansExample_02

PowerPoint: L14_08_PartitinoningClusteringAlgorithm

PowerPoint: L14_09_TypesOfClustering_01

PowerPoint: L14_09_TypesOfClustering_02

PowerPoint: L14_10_ApplicationsOfClustering

video: L14_01ClusterAnalysisAlgorithm
video: L14_02_ClusterAnalysisIntroPlottingOfObjects_02    |  

video: L14_02ClusterAnalysisIntroPlottingOfObjects
video: L14_03_ClusterAnalysisPreProcessingOfData_02   |  

video: L14_03ClusterAnalysisPreProcessingCharacteristicsOfData
video: L14_04ClusterAnalysisTypesOfClusters
video: L14_05PartitioningClusteringKMeans
video: L14_06PartitioningClusteringKMeansContinued

video: L14_07_KMeansExample_02

video: L14_08_PartitinoningClusteringAlgorithm

video: L14_09_TypesOfClustering_02    |  

video: L14_09_TypesOfClustering

video: L14_10_ApplicationsOfClustering

PlayCode: 10_L14_ClusterAnalysis     |      video: L14_ClusterAnalysis_CodeDemo_01

Partitioning Clustering - K-Means Example

VideoCase 10 : K-Means_Clustering

video: L15_01KMeansExampleProblemPart1
video: L15_02KMeansExampleProblemPart2
video: L15_03KMeansExampleProblemPart3

Apr 20

Group_09 Moderator

Clustering Techniques (Continued)

PowerPoint: L16_01_HirearchialClustering_Introduction_02

PowerPoint: L16_02_HirearchialClustering_AgglomerativeClustering_02

PowerPoint: L16_04_HirearchialClustering_ClusteringTechniques

Powerpoint: L16_05_HirearchialClustering_SpaceAndTimeComplexity

PowerPoint: L16_06_HirearchialClustering_DBSCAN

PowerPoint: L16_07_HirearchialClustering_MeasuresOfClusterValidity

PowerPoint: L16_08_HirearchialClustering_StepsInClustering

video: L16_01_HirearchialClustering_Introduction_02    |   

video: L16_01HierarchicalCustering
video: L16_02_HirearchialClustering_AgglomerativeClustering_02    |    

video: L16_02HierarchicalClusteringAgglomerativeProximityMatrix
video: L16_03HierarchicalCusteringInterClusterDistances

video: L16_04_HirearchialClustering_ClusteringTechniques

video: L16_05_HirearchialClustering_SpaceAndTimeComplexity

video: L16_06_HirearchialClustering_DBSCAN

video: L16_07_HirearchialClustering_MeasuresOfClusterValidity

video: L16_08_HirearchialClustering_StepsInClustering

PlayCode: 09_L16_HierarchialClustering    |     video: L16_HirearchialClustering_Codedemo_02

                                                                     video: L16_HirearchialClustering_Codedemo_01

Hierarchical Clustering - Single Link Example
Exercise:   16.    Chapter 8

video: L17_01HierarchicalClusteringSingleLinkExamplePart1
video: L17_02HierarchicalClusteringSingleLinkExamplePart2
video: L17_03HierarchicalClusteringSingleLinkExamplePart3
video: L17_04HierarchicalClusteringSingleLinkExamplePart4

Apr 27

TV Trees

Group_05 Moderator

Evaluation Methods

GroupActivity_05 : download WEKA software , and ORANGE software - run clustering, association rules discovery, and a decision tree
( use one of the datasets - of your choice  - which are pre-loaded in RSES )
// to turn in :  save your  WEKA  and  Orange  project files  ( go to File | Save As ) , and upload both your  WEKA  and  Orange  project  files to Canvas , also take screen shots and upload the screen shots to Canvas
// one group member submits this Exercise for the whole group

video: Exercise_02_DecisionTree_OrangeDataMiningSoftware_DEMO

PowerPoint: L21_01_AssociationRulesAndDiscoveryTree
PowerPoint: L21_02_ClusteringUsingOrange
PowerPoint: L21_03_WEKA_Introduction
PowerPoint: L21_04_WEKA_ModelBuilding
PowerPoint: L21_05_BuldingModelsWithWEKA
PowerPoint: L21_06_WEKA_DecisionTree
PowerPoint: L21_07_WEKA_AnalysisOfData
PowerPoint: L21_08_WEKA_OrangeSoftware

video: L21_01_AssociationRulesAndDiscoveryTree
video: L21_02_ClusteringUsingOrange
video: L21_03_WEKA_Introduction
video: L21_04_WEKA_ModelBuilding
video: L21_05_BuldingModelsWithWEKA
video: L21_06_WEKA_DecisionTree
video: L21_07_WEKA_AnalysisOfData
video: L21_08_WEKA_OrangeSoftware

PlayCode: 05_L21_WEKA_Association   |     video: L21_WEKA_Association_CodeDemo

Preparing for Final Exam

video: L18 01 Final ID3 Part1
video: L18 02 Final ID3 Part2
video: L18 03 Final Rosetta

video: L18 03 Final Query Satisfying Objects
video: L18 05 Final KMeans Clustering Part1
video: L18 06 Final KMeans Clustering Part2
video: L18 07 Final Single Link Clustering Part1
video: L18 08 Final Single Link Clustering Part2
video: L18 09 Final Single Link Clustering Part3

May 04


Reading Day - No Classes

May 07

Final Exam
- access exam on Canvas
- exam starts from 8:00pm - 11:00pm
- allowed time for exam is:   3:00 hours

Syllabus Copyright 2015-2025 Angelina A Tzacheva.
No re-usage or reproduction without permission.