Data Mining MSCS (CS-4012 )

Course Description

This course introduces students to the concepts, techniques, and tools essential for discovering patterns and knowledge from large datasets. It emphasizes practical applications in real-world scenarios and provides insights into the data-driven decision-making process.

Key Topics Covered:

• Introduction to Data Mining:
  Concepts, importance, and applications of data mining in various domains.

• Data Preprocessing:
  Techniques for cleaning, integrating, reducing, and transforming data.

• Association Rule Mining:
  Algorithms for finding frequent itemsets and association rules, including Apriori and FP-Growth.

• Classification Techniques:
  Decision trees, support vector machines, k-nearest neighbors, and ensemble methods.

• Clustering Algorithms:
  Techniques for grouping data, including k-means, hierarchical clustering, and DBSCAN.

• Evaluation Metrics:
  Methods for assessing the quality of classification, clustering, and association rule results.

• Big Data and Advanced Topics:
  Introduction to big data frameworks like Hadoop and Spark for scalable data mining.

Learning Outcomes:

By the end of the course, students will:

• Understand the fundamental concepts of data mining and its applications.
• Gain expertise in preprocessing and preparing data for analysis.
• Apply classification, clustering, and association mining techniques to solve problems.
• Evaluate the performance and reliability of data mining models.
• Use data mining tools and frameworks for hands-on projects and case studies.

This course emphasizes practical implementations and encourages students to apply data mining techniques to real-world datasets.