Data Mining syllabus for MCA 1st Year 2nd Sem R20 regulation JNTUH

Data Mining detailed syllabus for Master of Computer Applications(MCA), R20 regulation has been taken from the JNTUH official website and presented for the students affiliated to JNTUH course structure. For Course Code, Subject Names, Theory Lectures, Tutorial, Practical/Drawing, Credits, and other information do visit full semester subjects post given below. The syllabus PDF files can also be downloaded from the universities official website.

For all other MCA 1st Year 2nd Sem syllabus for R20 regulation JNTUH, do visit MCA 1st Year 2nd Sem syllabus for R20 regulation JNTUH subjects. The detailed syllabus for data mining is as follows.

Prerequisites:

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Course Objectives:

• Learn data mining concepts understand association rules mining.
• Discuss classification algorithms learn how data is grouped using clustering techniques.
• To develop the abilities of critical analysis to data mining systems and applications.
• To implement practical and theoretical understanding of the technologies for data mining.
• To understand the strengths and limitations of various data mining models.

Course Outcomes:

• Ability to perform the preprocessing of data and apply mining techniques on it.
• Ability to identify the association rules, classification and clusters in large data sets.
• Ability to solve real world problems in business and scientific information using data mining.
• Ability to classify web pages, extracting knowledge from the web.

Unit -I

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Unit -II

Association Rules: Problem Definition, Frequent Item Set Generation, The APRIORI Principle, Support and Confidence Measures, Association Rule Generation; APRIOIRI Algorithm, The Partition Algorithms, FP-Growth Algorithms, Compact Representation of Frequent Item Set- Maximal Frequent Item Set, Closed Frequent Item Set.

Unit -III

Classification: Problem Definition, General Approaches to solving a classification problem, Evaluation of Classifiers, Classification techniques, Decision Trees-Decision tree Construction, Methods for Expressing attribute test conditions, Measures for Selecting the Best Split, Algorithm for Decision tree Induction; Naive-Bayes Classifier, Bayesian Belief Networks; K- Nearest neighbor classificationAlgorithm and Characteristics.

Unit -IV

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Unit -V

Web and Text Mining: Introduction, web mining, web content mining, web structure mining, we usage mining, Text mining -unstructured text, episode rule discovery for texts, hierarchy of categories, text clustering.

Text Books:

1. Data Mining- Concepts and Techniques- Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers, Elsevier, Edition, 2006.
2. Introduction to Data Mining, Pang-Ning Tan, Vipin Kumar, Michael Steinbanch, Pearson Education.
3. Data mining Techniques and Applications, Hongbo Du Cengage India Publishing.

Reference Books:

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
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For detail syllabus of all other subjects of Master of Computer Applications 1st Year, visit MCA 1st Year syllabus subjects.

For all MCA results, visit JNTUH MCA all years, and semester results from direct links.