Data Warehousing and Data Mining

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Data Warehousing and Data Mining

Data Warehousing and Data Mining

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Dr. Sujata Chakravarty

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Course Name : Data Warehousing and Data Mining

Code(Credit) : CUTM1035(2-2-0)

Course Outcomes

COs Course outcomes Mapping COs with POs (High-3, Medium-2, Low-1)
CO1 Gain the knowledge on Data Warehouse, Data Mining Principles PO1 (3)-Engineering Knowledge.
CO2 Analyze and describe complex data types with respect to spatial and web mining PO2(3)-Problem Analysis
CO3 Identify      appropriate       data      mining algorithms to solve real world problems PO2(3)Problem Analysis
CO4 Design data warehouse with dimensional modeling and apply OLAP operations PO3 (3)-Design / Development of Solutions
CO5 Benefit the   user   experiences   towards research and innovation integration PO4(2)-Investigations of Complex Problem

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
  5. Data Mining: Introductory and Advanced Topics by Margaret Dunham, Pearson

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: 1

Suppose that a data warehouse consists of four dimensions “customer”,product”,“sales_person”and “sales_time”, and the three measures Sales_Amt (in rupees), VAT (inrupees) and Payment_type (in rupees). Draw the different classes of schemas that are popularly used for modelling data warehouses.

Case Studies: 2

Assume that a data warehouse consists of the three dimensions “Time”, “Doctor” and“Patient”, and the two measures “count”, and “charge” where charge is the fee that a doctor charges a patient for a visit. Enumerate and draw all the schema diagrams this case.

Our Main Teachers

Dr. Sujata Chakravarty

HoD & Associate Professor, Department of CSE, SoET
VIEW PROFILE

Dr. Sujata Chakravarty is a Senior Member of IEEE. Her research area includes multidisciplinary fields like Application of Computational Intelligence and Evolutionary Computing Techniques in the field of Financial Engineering, Bio-medical data classification, Smart Agriculture, Intrusion Detection System in Computer-Network, Analysis and prediction of different financial time series data. She is a reviewer of many […]

Prof. Susanta Kumar Nayak Working as Assistant Professor in Department of Computer Science & Engineering, Centurion University of Technology & Management, paralakhemundi,odisha . He completed MCA, MBA(HR), Ph.D. [(Pursuing). He has been an Academician from last 16.5 years. Previously he Worked as Assistant Professor in MCA Programme in Bharati Vidyapeeth Deemed University, Pune,  Institute of Management Kolhapur […]