ISYS6201 Data Warehouse and Data Miningcurriculum.binus.ac.id/files/2015/09/CO-ISYS6201.pdf · Data Warehouse and Data Mining (4) Study Program Information Systems Effective Date

FM - BINUS - AA- FPA - 27/R2

Study Program Specific Outcomes

Course Outline

ISYS6201

Data Warehouse and Data Mining

(4) Study Program

Information Systems

Effective Date 01 September 2018 Revision 2

1. Course Description

This course comprises the basic concept and architecture of data warehouse; how to design a data warehouse and

requirements that needed to build a data warehouse. This course give student basic knowledge related with data

warehouse and skill how to design, build and implement data warehouse that appropriate to the need.

In addition this course consists of data mining concepts and techniques for extracting knowledge from data and pre-

processing the data before mining. Mining methods discussed in this class include: association mining, classification

and prediction and cluster analysis. Finally this course gives student competency to apply and solve problems by

applying data mining concepts and techniques (how to apply them and when they are applicable).

2. Graduate Competency

Each course in the study program contributes to the graduate competencies that are divided into employability and

entrepreneurial skills and study program specific outcomes, in which students need to have demonstrated by the time

they complete their course.

BINUS University employability and entrepreneurial skills consist of planning and organizing, problem solving and

decision making, self management, team work, communication, and initiative and enterprise.

2.1. Employability and Entrepreneurial Skills

Aspect Key Behaviour

2.2. Study Program Specific Outcomes

3. Topics

• The Data Warehouse Environment

• Design data warehouse with

• The Data Warehouse and

• The Distributed Data

• External Data and the Data

• Unstructured Data and the

• Data Warehouse Design

• Introduction/Overview of Data

• Getting to Know Your Data;

• Classification: Basic



• Mining Frequent Patterns,

• Cluster Analysis: Basic


ISYS6201 - Data Warehouse and Data Mining | 2 Course Outline

Study Program Information Systems - Bina Nusantara University

• Data Mining Trends and

4. Learning Outcomes

On successful completion of this course, student will be able to:

• LO 1: Define the basic concepts, architecture and techniques of data warehouse and data mining

• LO 2: Explain collection of data and techniques for pre-processing the data before using in data warehouse and

data mining

• LO 3: Design data warehouse and data mining model

• LO 4: Analyze the implementation of data warehouse and data mining techniques which appropriate to the need

5. Teaching And Learning Strategies

In this course, the lecturers might deploy several teaching learning strategis, including Lecture, Individual and Group

Presentation, Case Studies.

6. Textbooks and Other Resources

6.1 Textbooks

1. William H. Inmon. (2005). Building the data warehouse. 04. WILEY. Indianapolis. ISBN: 780764599446 .

The book in the first list is a must to have for each student.

6.2 Other Resources

1. Building the data warehouse

2. Classification: Basic Concepts - Bayes Classification Methods How it can be used in the real world

3. Classification: Basic Concepts - Decision Tree Induction and How it can be used in the Real World

4. Classification: Basic Concepts - Rule-Based Classification

5. Cluster Analysis: Basic Concepts and Methods and how it can be used in the real world

6. Data Mining Trends and Research Frontiers

7. Data Warehouse Design Review Checklist

8. Design data warehouse with Kimball approach

9. External Data and the Data Warehouse

10. Getting to Know Your Data; Data Pre-processing and Prepare your Data before Mining

11. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.80.3193...pdf

12. http://dbtr.cs.aau.dk/DBPublications/DBTR-18.pdf

13. http://hanson.gmu.edu/ijcai91.pdf

14. http://hms.liacs.nl/mgts2004/papers/kuba.pdf

15. http://inmoncif.com/inmoncif-old/www/library/whiteprs/ttbuild.pdf

16. http://subs.emis.de/LNI/Proceedings/Proceedings63/GI-Proceedings.63-21.pdf

17. http://technet.microsoft.com/en-us/library/cc917677.aspx

18. http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap1_intro.pdf

19. http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap2_data.pdf

20. http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap3_data_exploration.pdf

21. http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap4_basic_classification.pdf

22. http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap5_alternative_classification.pdf

23. http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap6_basic_association_analysis.pdf

24. http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap8_basic_cluster_analysis.pdf

25. http://www.aaai.org/Papers/AAAI/1988/AAAI88-108.pd

26. http://www.crmodyssey.com/Documentation/Documentation_PDF/Data%

27. http://www.cs.toronto.edu/vldb04/protected/eProceedings/contents/pdf/IND8P2.PDF

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.80.3193...pdf

http://dbtr.cs.aau.dk/DBPublications/DBTR-18.pdf

http://hanson.gmu.edu/ijcai91.pdf

http://hms.liacs.nl/mgts2004/papers/kuba.pdf

http://inmoncif.com/inmoncif-old/www/library/whiteprs/ttbuild.pdf

http://subs.emis.de/LNI/Proceedings/Proceedings63/GI-Proceedings.63-21.pdf

http://technet.microsoft.com/en-us/library/cc917677.aspx

http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap1_intro.pdf

http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap2_data.pdf

http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap3_data_exploration.pdf

http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap4_basic_classification.pdf

http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap5_alternative_classification.pdf

http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap6_basic_association_analysis.pdf

http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap8_basic_cluster_analysis.pdf

http://www.aaai.org/Papers/AAAI/1988/AAAI88-108.pd

http://www.crmodyssey.com/Documentation/Documentation_PDF/Data%25

http://www.cs.toronto.edu/vldb04/protected/eProceedings/contents/pdf/IND8P2.PDF




28. http://www.cse.usf.edu/~ytu/pub/MOUND09.pdf

29. http://www.dwreview.com/Articles/Project_Management.html

30. http://www.emis.de/journals/JEHPS/Decembre2008/Bock.pdf

31. http://www.executionmih.com/data-warehouse/project-scoping-planning.php

32. http://www.exinfm.com/pdffiles/intro_dm.pdf

33. http://www.fusion2004.foi.se/papers/IF04-0288.pdf

34. http://www.horsburgh.com/h_dataw.html

35. http://www.information-management.com/issues/20040801/1007228-1.html

36. http://www.languageatinternet.de/articles/2006/373

37. http://www.research.ibm.com/dar/papers/pdf/business_applications_of_dm.pdf

38. http://www.rimtengg.com/coit2007/proceedings/pdfs/94.pdf

39. http://www.youtube.com/watch?v=a5yWr1hr6QY

40. http://www.youtube.com/watch?v=q77B-G8CA24

41. http://www2.sas.com/proceedings/sugi22/DATAWARE/PAPER116.PDF

42. https://drive.google.com/drive/folders/1WcRTHu9fml9CnraaJQH_z95BHhgFp541

43. https://www.cu.edu/irm/stds/ddg/dzproces.html

44. Introduction/Overview of Data Mining and The example used

45. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods and How it can be

46. Star scheme design

47. The Data Warehouse and Technology, Now and Future

48. The Data Warehouse Environment

49. The Distributed Data Warehouse

50. Unstructured Data and the Data Warehouse

http://www.cse.usf.edu/~ytu/pub/MOUND09.pdf

http://www.dwreview.com/Articles/Project_Management.html

http://www.emis.de/journals/JEHPS/Decembre2008/Bock.pdf

http://www.executionmih.com/data-warehouse/project-scoping-planning.php

http://www.exinfm.com/pdffiles/intro_dm.pdf

http://www.fusion2004.foi.se/papers/IF04-0288.pdf

http://www.horsburgh.com/h_dataw.html

http://www.information-management.com/issues/20040801/1007228-1.html

http://www.languageatinternet.de/articles/2006/373

http://www.research.ibm.com/dar/papers/pdf/business_applications_of_dm.pdf

http://www.rimtengg.com/coit2007/proceedings/pdfs/94.pdf

http://www.youtube.com/watch?v=a5yWr1hr6QY

http://www.youtube.com/watch?v=q77B-G8CA24

http://www2.sas.com/proceedings/sugi22/DATAWARE/PAPER116.PDF

http://www.cu.edu/irm/stds/ddg/dzproces.html




7. Schedule

Lecture

Session/Mode Related LO Topics References

1

F2F

• LO 1

The Data Warehouse Environment

• Data Homogeneity/ Heterogeneity

• Data Warehouse: The Standards Manual

• Incorrect data in the Data warehouse

• Reporting and the Architecture Environment

• Structure of Data Warehouse

• Structuring Data in the Data Warehouse

• Subject Orientation

• The Data Warehouse

Environment

• Building the Data

Warehouse: Getting Started

http://inmoncif. com/inmoncif-

old/www/library/whiteprs/t

tbuild.pdf


Environment

: Quantifying Cost Justification

and Return on Investment

http://www.crmodyssey.

com/Documentation/Doc

umentation_PDF/Data%

20Warehouse_Environm

ent_Cost_Justification_an d_ROI.pdf

2

F2F

• LO 1

The Data Warehouse Environment

• Data Homogeneity/ Heterogeneity

• Data Warehouse: The Standards Manual

• Incorrect data in the Data warehouse

• Reporting and the Architecture Environment

• Structure of Data Warehouse

• Structuring Data in the Data Warehouse

• Subject Orientation


Environment

• Building the Data

Warehouse: Getting Started

http://inmoncif. com/inmoncif-

old/www/library/whiteprs/t

tbuild.pdf


Environment

: Quantifying Cost Justification

and Return on Investment

http://www.crmodyssey.

com/Documentation/Doc

umentation_PDF/Data%

20Warehouse_Environm

ent_Cost_Justification_an d_ROI.pdf

3

F2F

• LO 2

• LO 3

• LO 4

Design data warehouse with Kimball approach

• Beginning with Operational Data

• Complexity of Transformation and Integration

• Data/Process Models and the

Architecture Environment

• Going from the Data

Warehouse to the Operational

Environment

• Meta Data

• Design data warehouse

with Kimball approach

• Data Warehouse Design

Process https://www.cu.

edu/irm/stds/ddg/dzproce

http://inmoncif/

http://inmoncif/




• Normalization/Denormalization

• Star Joins

• Supporting the ODS

• The Data Model and Iterative Development

• The Data Warehouse and Data Models

s.html

• Data Warehouse

Design

http://www.horsburgh.

com/h_dataw.html

• Star scheme design

• Dimensional Model

(Star Scheme Design)

https://drive.google.

com/drive/folders/1WcRT

Hu9fml9CnraaJQH_z95B

HhgFp541

4

F2F

• LO 2

• LO 3

• LO 4

Design data warehouse with Kimball approach

• Beginning with Operational Data

• Complexity of Transformation and Integration

• Data/Process Models and the

Architecture Environment

• Going from the Data Warehouse to

the Operational Environment

• Meta Data

• Normalization/Denormalization

• Star Joins

• Supporting the ODS

• The Data Model and Iterative Development

• The Data Warehouse and Data Models

• Design data

warehouse with Kimball

approach

• Data Warehouse

Design Process

https://www.cu.

edu/irm/stds/ddg/dzproce

s.html

• Data Warehouse

Design

http://www.horsburgh.

com/h_dataw.html

• Star scheme design

• Dimensional Model

(Star Scheme Design)

https://drive.google.

com/drive/folders/1WcRT

Hu9fml9CnraaJQH_z95B

HhgFp541

5

F2F

• LO 1

• LO 2

The Data Warehouse and Technology, Now and

Future

• Capturing and Managing Contextual

Information

• Context and Content

• Data Warehousing across Multiple Storage Media

• Index/Monitor Data

• Managing Large Amounts of Data

• Managing Multiple Media

• Meta Data in the Data Warehouse Environment

• Multidimensional DBMS and the Data Warehouse

• Refreshing the Data Warehouse

• Testing

• The Data

Warehouse and

Technology, Now and

Future

• An Overview of Data

Warehousing and OLAP

Technology

http://citeseerx.ist.psu.

edu/viewdoc/download?

doi=10.1.1.80.3193...pdf

• Technology Challenges

in a Data Warehouse

http://www.cs.toronto.

edu/vldb04/protected/ePr

oceedings/contents/pdf/IND

8P2.PDF

http://citeseerx.ist.psu/

http://www.cs.toronto/




6

F2F

• LO 1

• LO 2

The Data Warehouse and Technology, Now and

Future

• Capturing and Managing Contextual Information

• Context and Content

• Data Warehousing across Multiple Storage Media

• Index/Monitor Data

• Managing Large Amounts of Data

• Managing Multiple Media

• Meta Data in the Data Warehouse Environment

• Multidimensional DBMS and the Data Warehouse

• Refreshing the Data Warehouse

• Testing

• The Data

Warehouse and

Technology, Now and

Future

• An Overview of Data

Warehousing and OLAP

Technology

http://citeseerx.ist.psu.

edu/viewdoc/download?

doi=10.1.1.80.3193...pdf

• Technology

Challenges in a Data

Warehouse

http://www.cs.toronto.

edu/vldb04/protected/ePr

oceedings/contents/pdf/I ND8P2.PDF

7

GSLC

• LO 2

The Distributed Data Warehouse

• Building the Warehouse on Multiple Levels

• Distributed Data Warehouse Development

• Types of Distributed Data Warehouses


Warehouse

• Distributed Data

Warehousing with

Microsoft SQL Server

2000 and Windows 2000

Data centre Server

http://technet.microsoft.

com/en-

us/library/cc917677.aspx

• Designing

Distributed Data

Warehouses and OLAP

Systems

http://subs.emis.

de/LNI/Proceedings/Proc

eedings63/GI- Proceedings.63-21.pdf

8

GSLC

• LO 2

The Distributed Data Warehouse

• Building the Warehouse on Multiple Levels

• Distributed Data Warehouse Development

• Types of Distributed Data Warehouses


Warehouse

• Distributed Data

Warehousing with

Microsoft SQL Server

2000 and Windows 2000

Data centre Server

http://technet.microsoft.

com/en-

us/library/cc917677.aspx

• Designing

http://citeseerx.ist.psu/

http://www.cs.toronto/

http://technet.microsoft/

http://subs.emis/

http://technet.microsoft/




Distributed Data

Warehouses and OLAP

Systems

http://subs.emis.

de/LNI/Proceedings/Proc

eedings63/GI-

Proceedings.63-21.pdf

9

F2F

• LO 2

External Data and the Data Warehouse

• Comparing Internal Data to External Data

• Different Components of External Data

• External Data in the Data Warehouse

• Meta Data and External Data

• Modelling and External Data

• Storing External Data

• External Data and

the Data Warehouse

• Incorporating

External Data Into the

Data Warehouse

http://www2.sas.

com/proceedings/sugi22/

DATAWARE/PAPER116.

PDF

• Incorporating

External Data into Data

Warehouses – Problems

Identified and

Contextualized

http://www.fusion2004.foi. se/papers/IF04-0288.pdf

10

F2F

• LO 2

Unstructured Data and the Data Warehouse

• A Self-Organizing Map (SOM)

• A Themed Match

• A Two-Tiered Data Warehouse

• Fitting the Two Environments Together

• Integrating the Two Worlds

• Unstructured Data

and the Data Warehouse

• Looking Ahead:

Unstructured Data

http://www.information-

management.

com/issues/20040801/10

07228-1.html

• Integrating

Structured and

Unstructured Data in a

Business Intelligence

System

http://www.

languageatinternet. de/articles/2006/373

11

GSLC

• LO 3

• LO 4

Data Warehouse Design Review Checklist

• A Typical Data Warehouse Design Review

• Administering the Review

• The Results

• What Should the Agenda Be?

• When to Do Design Review

• Who Should Be in the Design Review?

• Data Warehouse

Design Review Checklist

• Data Warehouse

Project Scoping and

Planning

http://www.executionmih.

com/data-

warehouse/project-

http://subs.emis/

http://www2.sas/

http://www.fusion2004.foi/

http://www/




scoping-planning.php

• Data Warehouse

Project Management

http://www.dwreview.

com/Articles/Project_Man

agement.html

12

GSLC

• LO 3

• LO 4

Data Warehouse Design Review Checklist

• A Typical Data Warehouse Design Review

• Administering the Review

• The Results

• What Should the Agenda Be?

• When to Do Design Review

• Who Should Be in the Design Review?

• Data Warehouse

Design Review Checklist

• Data Warehouse

Project Scoping and

Planning

http://www.executionmih.

com/data-

warehouse/project-

scoping-planning.php

• Data Warehouse

Project Management

http://www.dwreview.

com/Articles/Project_Man agement.html

13

F2F

• LO 1

Introduction/Overview of Data Mining and The example

used

• Classification of Data Mining Systems

• Data Mining – On What Kind of Data?

• Data Mining Functionalities

• Integration of a Data Mining Systems with

a Database or Data Warehouse Systems

• Major Issues in Data Mining

• What Motivated Data Mining?

•

Introduction/Overview of

Data Mining and The

example used

• Introduction to Data

Mining

http://www-users.cs.umn.

edu/~kumar/dmbook/dms

lides/chap1_intro.pdf

• Chapter I:

Introduction to Data

Mining

http://www.exinfm. com/pdffiles/intro_dm.pdf

14

F2F

• LO 2

Getting to Know Your Data; Data Pre-processing and

Prepare your Data before Mining

• Basic Statistical Descriptions of Data

• Data Cleaning

• Data Discretization and Concept

Hierarchy Generation

• Data Integration and Transformation

• Data Objects and Attribute Types

• Data Reduction

• Data Visualization

• Measuring Data Similarity and Dissimilarity

• Why Pre-processing the Data?

• Getting to Know

Your Data; Data Pre-

processing and Prepare

your Data before Mining

• Data



lides/chap2_data.pdf

• Exploring Data



lides/chap3_data_explora tion.pdf

http://www-users.cs.umn/






15

GSLC

• LO 3

• LO 4

Classification: Basic Concepts - Decision Tree

Induction and How it can be used in the Real World

• Attribute Selection Measures

• Basic Concepts

• Decision Tree Induction

• Scalability and Decision Tree Induction

• Tree Pruning

• Visual Mining for Decision Tree Induction


Concepts - Decision Tree

Induction and How it can

be used in the Real

World

• Decision Tree

Tutorial in 7 minutes with

Decision Tree Analysis &

Decision Tree Example

http://www.youtube.

com/watch?

v=a5yWr1hr6QY

• Data Mining

Classification: Basic

Concepts, Decision

Trees, and Model

Evaluation



lides/chap4_basic_classif ication.pdf

16

GSLC

• LO 3

• LO 4

Classification: Basic Concepts - Decision Tree

Induction and How it can be used in the Real World

• Attribute Selection Measures

• Basic Concepts

• Decision Tree Induction

• Scalability and Decision Tree Induction

• Tree Pruning

• Visual Mining for Decision Tree Induction


Concepts - Decision Tree

Induction and How it can

be used in the Real

World

• Decision Tree

Tutorial in 7 minutes with

Decision Tree Analysis &

Decision Tree Example

http://www.youtube.

com/watch?

v=a5yWr1hr6QY

• Data Mining

Classification: Basic

Concepts, Decision

Trees, and Model

Evaluation



lides/chap4_basic_classif ication.pdf

17

F2F

• LO 3

• LO 4

Classification: Basic Concepts - Rule-Based

Classification

• Rule Extraction from a Decision Tree

• Rule Induction Using Sequential Covering

Algorithm

• Using IF-THEN Rules for Classification


Concepts - Rule-Based

Classification

• Classification:

Alternative Techniques








lides/chap5_alternative_c

lassification.pdf

• A Rule-Based

Classification Algorithm

for Uncertain Data

http://www.cse.usf.

edu/~ytu/pub/MOUND09. pdf

18

F2F

• LO 3

• LO 4

Classification: Basic Concepts - Rule-Based

Classification

• Rule Extraction from a Decision Tree

• Rule Induction Using Sequential Covering

Algorithm

• Using IF-THEN Rules for Classification


Concepts - Rule-Based

Classification

• Classification:

Alternative Techniques



lides/chap5_alternative_c

lassification.pdf

• A Rule-Based

Classification Algorithm

for Uncertain Data

http://www.cse.usf.

edu/~ytu/pub/MOUND09. pdf

19

F2F

• LO 3

• LO 4

Classification: Basic Concepts - Bayes Classification

Methods How it can be used in the real world

• Bayes Theorem

• Model Evaluation and Selection

• Naïve Bayesian Classification


Concepts - Bayes

Classification Methods

How it can be used in the

real world

• Bayesian

Classification Theory

http://hanson.gmu.

edu/ijcai91.pdf

• Bayesian

Classification

http://www.aaai.

org/Papers/AAAI/1988/A AAI88-108.pd

20

F2F

• LO 3

• LO 4

Classification: Basic Concepts - Bayes Classification

Methods How it can be used in the real world

• Bayes Theorem

• Model Evaluation and Selection

• Naïve Bayesian Classification


Concepts - Bayes

Classification Methods

How it can be used in the

real world

• Bayesian

Classification Theory

http://hanson.gmu.


http://www.cse.usf/


http://www.cse.usf/

http://hanson.gmu/

http://hanson.gmu/




edu/ijcai91.pdf

• Bayesian

Classification

http://www.aaai.

org/Papers/AAAI/1988/A

AAI88-108.pd

21

F2F

• LO 3

• LO 4

Mining Frequent Patterns, Associations, and

Correlations: Basic Concepts and Methods and How it

can be used in the real world

• Basic Concepts

• Frequent Itemset Mining Methods

• Which Patterns Are Interesting? Pattern

Evaluation Methods

• Mining Frequent

Patterns, Associations,

and Correlations: Basic

Concepts and Methods

and How it can be used

in the real world

• Mining Frequent

Patterns in Object-

Oriented Data

http://hms.liacs.

nl/mgts2004/papers/kuba

.pdf

• Association

Analysis: Basic Concepts

and Algorithms



lides/chap6_basic_associ

ation_analysis.pdf

22

F2F

• LO 3

• LO 4

Mining Frequent Patterns, Associations, and

Correlations: Basic Concepts and Methods and How it

can be used in the real world

• Basic Concepts

• Frequent Itemset Mining Methods

• Which Patterns Are Interesting? Pattern

Evaluation Methods

• Mining Frequent

Patterns, Associations,

and Correlations: Basic

Concepts and Methods

and How it can be used

in the real world

• Mining Frequent

Patterns in Object-

Oriented Data

http://hms.liacs.

nl/mgts2004/papers/kuba

.pdf

• Association

Analysis: Basic Concepts

and Algorithms



lides/chap6_basic_associ

ation_analysis.pdf

23

F2F

• LO 3

• LO 4

Cluster Analysis: Basic Concepts and Methods and

how it can be used in the real world

• Density-Based Methods

• Evaluation of Clustering

• Cluster Analysis:

Basic Concepts and

Methods and how it can

be used in the real world

http://hms.liacs/


http://hms.liacs/





• Hierarchical Methods

• Outlier and Outlier Analysis

• Outlier Detection - Classification Approaches

• Outlier Detection - Clustering-Base Approaches

• Outlier Detection Methods

• Partitioning Methods

• Statistical Approaches

• What is Cluster Analysis?


Basic Concepts and

Algorithms



lides/chap8_basic_cluster

_analysis.pdf

• A Comparison of

Hierarchical Methods for

Clustering Functional

Data

http://www.emis.

de/journals/JEHPS/Dece

mbre2008/Bock.pdf

24

F2F

• LO 3

• LO 4

Cluster Analysis: Basic Concepts and Methods and

how it can be used in the real world

• Density-Based Methods

• Evaluation of Clustering

• Hierarchical Methods

• Outlier and Outlier Analysis

• Outlier Detection - Classification Approaches

• Outlier Detection - Clustering-Base Approaches

• Outlier Detection Methods

• Partitioning Methods

• Statistical Approaches

• What is Cluster Analysis?


Basic Concepts and

Methods and how it can

be used in the real world


Basic Concepts and

Algorithms



lides/chap8_basic_cluster

_analysis.pdf

• A Comparison of

Hierarchical Methods for

Clustering Functional

Data

http://www.emis.

de/journals/JEHPS/Dece

mbre2008/Bock.pdf

25

F2F

• LO 1

Data Mining Trends and Research Frontiers

• Data Mining Applications

• Data Mining Trends

• Other Methodologies of Data Mining


and Research Frontiers

• Applications &

Trends in Data Mining,

http://www.rimtengg.

com/coit2007/proceeding

s/pdfs/94.pdf

• Business

Applications of Data

Mining

http://www.research.ibm.

com/dar/papers/pdf/busin

ess_applications_of_dm. Pdf



http://www.research.ibm/




26

F2F

• LO 1

Data Mining Trends and Research Frontiers

• Data Mining Applications


Other Methodologies of Data Mining

• Data Mining Trends and Research Frontiers

• Applicatio

ns &

Trends in

Data

Mining,

http://www

.rimtengg.

com/coit20

07/procee

ding

s/pdfs/94.

pdf

• Business Applications of Data Mining http://www.research.ibm. com/dar/papers/pdf/busin ess_applications_of_dm. pdf

8. Evaluation

Lecture

Final Evaluation Score

Aspects Weight

Theory 100%

9. Assessment Rubric (Study Program Specific Outcomes)

LO

Indicators

Proficiency Level

Excellent (85 - 100)

Good (75 - 84)

Average (65 - 74)

Poor (<= 64)

LO1

1.1. 1.1 Ability to define data

warehouse concept and architecture

All descriptions

are clearly

stated

Many

descriptions are

competent

Few

descriptions are

complete

The

descriptions are

inadequate and

not in logical consistency

1.2. 1.2 Ability to define data mining

concepts and techniques

All descriptions

are clearly

stated

Many

descriptions are

competent

Few

descriptions are

complete

The

descriptions are

inadequate and

not in logical consistency

Assessment Activity

LO

1 2 3 4

ASSIGNMENT

FINAL EXAM

MID EXAM



Documents

ISYS6201 Data Warehouse and Data Miningcurriculum.binus.ac.id/files/2015/09/CO-ISYS6201.pdf · Data Warehouse and Data Mining (4) Study Program Information Systems Effective Date