5308: Data Mining Lab Syllabus for Robotics Process Automation 5th Sem 2021 Revision SITTTR

Data Mining Lab detailed syllabus for Robotics Process Automation (RP) for 2021 revision curriculum has been taken from the SITTTRs official website and presented for the Robotics Process Automation students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Robotics Process Automation 5th Sem scheme and its subjects, do visit Robotics Process Automation (RP) 5th Sem 2021 revision scheme. The detailed syllabus of data mining lab is as follows.

Course Objectives:

• Learn to perform data mining tasks using a data mining toolkit (such as open source WEKA).
• Understand the data sets and data preprocessing.
• Demonstrate the working of algorithms for data mining tasks such association rule mining, classification, clustering and regression.
• Exercise the data mining techniques with varied input values for different parameters.
• To obtain Practical Experience Working with all real data sets.

Course Outcomes:

On completion of the course student will be able to:

1. Use different features of WEKA tool
2. Preprocess the data for mining
3. Determine association rules
4. Model various classifiers & Examine clusters from the available data

Module 1:

1. Basics of WEKA tool a. Investigate the Application interfaces. b. Explore the default datasets
2. Creating a new Arff File

Module 2:

1. Pre-process a given dataset based on Attribute Selection
2. Pre-process a given dataset based on Handling missing values
3. Pre-process a given dataset based on Discretization

Module 3:

1. Calculate information Gain
2. Build a Decision Tree using ID3 algorithm.
3. Generate Association Rules using the FP-Growth algorithm.
4. Accuracy and error Measures evaluation

Module 4:

1. Demonstrate classification process on a given dataset using Naive Bayesian Classifier.
2. Demonstrate classification process on a given dataset using Nearest neighbor Classifier.
3. Build a distance matrix of the given data using various distance measures.
4. Cluster the given dataset by using the k-Means algorithm and visualize the cluster mean values and standard deviation of dataset attributes.
5. Cluster the given dataset using a hierarchical clustering algorithm.

Micro Project

Students are expected to do a micro project in data mining during the course for the purpose of continuous evaluation. This experiment shall be included in the bona-fide record.
Example: Develop program such as

• Disease Prediction
• Housing Price Prediction
• Fake News Detection

Text Books:

1. Dunham M H, “Data Mining: Introductory and Advanced Topics”, Pearson Education, New Delhi, 2003.
2. Jaiwei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, Elsevier, 2006.

Online Resources

1. https://www.investopedia.com/terms/d/datamining.asp
2. https://www.techtarget.com/
3. https://www.javatpoint.com/data-mining
4. https://www.electronicshub.org/artificial-neural-networks-ann/
5. https://www.tutorialspoint.com/data_mining/
6. https://towardsdatascience.com/the-fp-growth-algorithm

List of Experiments:

1. Basics of WEKA tool a. Investigate the Application interfaces. b. Explore the default datasets.
2. Pre-process a given dataset based on the following: a. Attribute Selection b. Handling Missing Values
3. Pre-process a given dataset based on the following: a. Discretization b. Eliminating Outliers
4. Create a dataset in ARFF (Attribute-Relation File Format) for any given dataset and perform Market-Basket Analysis.
5. Generate Association Rules using the FP-Growth algorithm.
6. Build a Decision Tree using ID3 algorithm.
7. Demonstrate classification process on a given dataset using Naive Bayesian Classifier.
8. Demonstrate classification process on a given dataset using Nearest neighbor Classifier.
9. Build a distance matrix of the given data using various distance measures.
10. Cluster the given dataset by using the k-Means algorithm and visualize the cluster mean values and standard deviation of dataset attributes.
11. Cluster the given dataset using a hierarchical clustering algorithm.

For detailed syllabus of all other subjects of Robotics Process Automation (RP), 2021 revision curriculum do visit Robotics Process Automation 5th Sem subject syllabuses for 2021 revision.

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