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DWDM Lab QP 3-1

The document outlines the lab activities for the Data Warehousing and Data Mining course at Andhra Loyola Institute of Engineering and Technology for the academic year 2024-25. It includes tasks such as exploring the WEKA toolkit, writing Python and Java programs for various data mining algorithms, and visualizing datasets using different methods. The activities aim to enhance students' practical skills in data mining and machine learning techniques.

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Mrunni Mrinalini
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61 views1 page

DWDM Lab QP 3-1

The document outlines the lab activities for the Data Warehousing and Data Mining course at Andhra Loyola Institute of Engineering and Technology for the academic year 2024-25. It includes tasks such as exploring the WEKA toolkit, writing Python and Java programs for various data mining algorithms, and visualizing datasets using different methods. The activities aim to enhance students' practical skills in data mining and machine learning techniques.

Uploaded by

Mrunni Mrinalini
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as DOCX, PDF, TXT or read online on Scribd
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ANDHRA LOYOLA INSTITUTE OF ENGINEERING AND TECHNOLOGY

DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING

Year/Sem: III Year – 1 Sem Dt: 28-10-2024


Class: III CSE Sec-2 AC Yr: 2024-25
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DATA WAREHOUSING AND DATA MINING LAB

1 . Explore machine learning tool “WEKA” Explore WEKA Data Mining/Machine Learning Toolkit.
Downloading and/or installation of WEKA data mining toolkit. Understand the features of WEKA
toolkit such as Explorer, Knowledge Flow interface, Experimenter, command-line interface.
Navigate the options available in the WEKA (ex. Select attributes panel, Preprocess panel, Classify
panel, Cluster panel, Associate panel and Visualize panel) Study the arff file format Explore the
available data sets in WEKA. Load a data set (ex. Weather dataset, Iris dataset, etc.) Load each
dataset and observe the following:
1. List the attribute names and their types
2. Number of records in each dataset
3. Identify the class attribute (if any)
4. Plot Histogram
5. Determine the number of records for each class.
6. Visualize the data in various dimensions

2. Write a Python program to generate frequent item sets / association rules using Apriori algorithm

3. Write a program to calculate chi-square value using Python. Report your observation.

4. Write a program of Naive Bayesian classification using Python programming language.

5. Implement a Java program to perform Apriori algorithm

6. Write a program to cluster your choice of data using simple k-means algorithm using JDK

7. Write a program of cluster analysis using simple k-means algorithm in Python Language.

8. Write a program to compute/display dissimilarity matrix (for your own dataset containing at least
four instances with two attributes) using Python

9. Visualize the datasets using matplotlib in python (Histogram, Box plot, Bar chart, Pie chart etc.,)

10. Write a java program to prepare a simulated data set with unique instances.

INTERNAL EXAMINER Head Of Department

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