Data Warehousing and Data Mining

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Data Warehousing and Data Mining

Data Warehousing and Data Mining

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Dr. Sangram Keshari Swain

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Course Name : Data Warehousing and Data Mining

Code(Credit) : ABC01(2-2-0)

Course Objectives

  • To identify the scope and essentiality of Data Warehousing and Mining.
  • To analyze data, choose relevant models and algorithms for respective applications.
  • To study spatial and web data mining.
  • To develop research interest towards advances in data mining.

Learning Outcomes

  • Understand Data Warehouse, Data Mining Principles
  • Design data warehouse with dimensional modeling and apply OLAP operations.
  • Identify appropriate data mining algorithms to solve real world problems
  • Can access the data from different files like Excel, Word, SQL, PDF etc.
  • Describe complex data types with respect to spatial and web mining
  • Benefit the user experiences towards research and innovation integration

Course Syllabus

Module I:  Data Mining Functionalities (09 hrs)

Data Mining Functionalities, Data Mining Task Primitives, Integration of a Data Mining System with a Database or a Data Warehouse System, Major issues in Data Mining. Data Pre-processing: Need for Pre-processing the data, Data Cleaning, Data Integration and Transformation, Data Reduction, Discretization and Concept Hierarchy Generation.

Practice:

  1. Write a program to demonstrate Pre-processing program on Cancer.arff
  2. Write a program to demonstrate Visualization on Crop-yield. Arff.

Module II: Data Warehouse (09 hrs)
Data warehousing; The need for Data Warehousing, the Building blocks of Data Warehouse, Data Warehouses and Data Marts, an overview of the components, metadata in the Data Warehouse, trends in Data Warehousing, Multidimensional Data Model, Data Warehousing Architecture, Data Warehouse Implementation, Further Development of Data Cube Technology, From Data Warehousing to Data Mining, Data Cube Computation and Data Generalization

Practice:

  1. Write a program to analyze Weather.arff using Naive Bayesian algorithm.
  2. Write a program to analyze Covid.arff using J-48.

Module III: OLAP Technology for Data Mining  (07 hrs)

Data Warehouse and OLAP Technology, MOLAP, ROLAP, HOLAP, Difference between OLTP and OLAP.

Practice:

  1. Do Analytical operations in 1) Roll-up 2) Drill-down 3) Slice 4) Dice and 5) Pivot.

Module IV: Mining Frequent Patterns, Associations and Correlations (07 hrs)

Mining Frequent Patterns, Associations and Correlations: Efficient and Scalable Frequent Itemset, Mining Methods, Mining various kinds of Association Rules, From Association Mining to Correlation Analysis, Constraint-Based Association Mining.

Practice:

  1. Write a program to demonstrate association rule mining using Apriori algorithm (Market-basket-analysis.arff).
  2. Writing programs to access the data from PDF and SQL file.

Module V: Accessing data from different databases (07 hrs)

Accessing data from Excel file, Notepad file, Access file, Word file, SQL file, PDF file and Image file.

Practice:

  1. Writing programs to access the data from Excel, Notepad, Access and Word file.
  2. Writing programs to write program output into different files, appending columns (output/results) into an existing file using Python.
  3. Write a program to demonstrate Pre-processing on Soil.arff

Module VI: Mining Streams, Time Series and Sequence Data (08 hrs)

Mining Streams, Time Series and Sequence Data: Mining Data Streams, Mining Time- Series, Data, Mining Sequence Patterns in Transactional Databases, Mining Sequence Patterns in Biological Data, Graph Mining, Social Network Analysis Multi Relational Data Mining and Spatial Data Mining.

Practice:

  1. Write a program to demonstrate Pre-processing and Visualization on Student
    arff

Module VII: Mining Object, Spatial, Multimedia, Text and Web Data (07 hrs)
Multidimensional Analysis and Descriptive Mining of Complex Data Objects, Spatial Data Mining, Multimedia Data Mining, Text Mining, Mining the World Wide Web, Applications and Trends in Data Mining.

Practice:

  1. Write programs to analyze text dataset.

TEXT BOOKS:

  1. Data Mining Concepts and Techniques - Jiawei Han &MichelineKamber, Morgan Kaufmann Publishers, Elsevier,2nd Edition, 2006.
  2. Introduction to Data Mining Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Pearson education.

REFERENCE BOOKS:

  1. Data Mining Techniques Arun K Pujari,2nd edition, Universities Press.
  2. Data Warehousing in the Real World Sam Aanhory& Dennis Murray Pearson Edn Asia.
  3. Insight into Data Mining, K.P.Soman, S.Diwakar,V.Ajay,PHI,2008.
  4. Data Warehousing Fundamentals Paulraj Ponnaiah Wiley student Edition

Session 5 & 6

Practice 1:

Write a program to demonstrate Pre-processing program on Cancer.arff

Session 9

Practice 2:

Write a program to demonstrate Visualization on Crop-yield. Arff

Session 14 & 15

Practice 3:

Write a program to analyze Weather.arff using Naive Bayesian algorithm.

Session 17 & 18

Practice 4:

Write a program to analyze Covid.arff using J-48.

Session 19 & 20

OLAP Technology

https://intellipaat.com/blog/tutorial/data-warehouse-tutorial/what-is-olap-and-multidimensional-model/

https://www.youtube.com/watch?v=pM--s4szPnQ

MOLAP

https://www.youtube.com/watch?v=LzmAbi5ZOhE

https://www.guru99.com/multidimensional-online-analytical-processing.html

Session 24 & 25

Practice 5:

Do Analytical operations in 1) Roll-up 2) Drill-down 3) Slice 4) Dice and 5) Pivot

Session 28 & 29

Practice 6:

Write a program to demonstrate association rule mining using Apriori algorithm (Market-basket-analysis.arff).

Session 31 & 32

Practice 7:

Writing programs to access the data from PDF and SQL file.

Session 34 & 35

Practice 8:

Writing programs to access the data from Excel, Notepad, Access and Word file.

Session 36 & 37

Practice 8:

Writing programs to write output into different files, appending columns (output/results) into existing file using Python.

Session 38 & 39

Practice 8:

Write a program to demonstrate Pre-processing on Soil.arff.

Session 44 & 45

Practice 11:

Write a program to demonstrate Pre-processing and Visualization on Student.arff

Session 48 & 49

Session 50 & 51

Spatial Data Mining, Multimedia Data Mining, Text Mining, Mining the World Wide Web, 

https://www.youtube.com/watch?v=hb3egqqSTQ4

Session 53 & 54

Practice 13:

Write programs to analyze text dataset.

Case Studies

Case Studies

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    Dr. Sangram Keshari Swain

    Dean, Students' Welfare and Professor In-Charge, Examinations and Associate Professor, Department of Computer Science and Engineering
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    Ph.D. in Computer Science and Engineering having 12 years of teaching experience having a demonstrated history of working in the higher education industry. Skilled in Social Services, Teaching, Research, Data Analysis, and Higher Education. A strong education professional having multidimensional approaches like Engineering, Technology, Management, Social Service and Law. Social Responsibility activity has been my […]