2025-26 II-I VR23-OBJECT-ORIENTED PROGRAMMING THROUGH JAVA LAB

Netaji_VR23 - OOPS JAVA LAB

Course Objectives:

• Practice object-oriented programming in the Java programming language.
• Implement Classes, Objects, Methods, Inheritance, Exception Handling, and Runtime Polymorphism.
• Illustrate JDBC connectivity and user-defined exception mechanisms.
• Construct Threads, Event Handling, and JavaFX GUI interfaces.

Topics Covered:

• OOP Fundamentals - data types, control structures
• Classes, Methods, Objects, Inheritance, Polymorphism
• Exception Handling, Threads, Packages, Interfaces
• File Handling, I/O Streams, JavaFX GUI

Sample Lab Exercises:

Exercise 1:

1. Display default values of all primitive data types in Java.
2. Program to find the roots of a quadratic equation and describe the nature of roots based on discriminant.

Exercise 2:

1. Binary search implementation in Java.
2. Bubble sort implementation.
3. StringBuffer operations - delete and remove characters.

Exercise 3:

1. Implementing a class with methods and invoking them in main.
2. Method Overloading.
3. Constructors and Constructor Overloading.

Exercise 4:

1. Single Inheritance.
2. Multilevel Inheritance.
3. Abstract class to find areas of different shapes.

Exercise 5:

1. Example of “super” keyword.
2. Interface implementation and its inheritance capabilities.
3. Runtime Polymorphism.

Exercise 6:

1. Exception Handling mechanism.
2. Multiple catch clauses.
3. Java Built-in Exceptions.
4. User Defined Exceptions.

Exercise 7:

1. Create threads using both Thread class and Runnable interface to display messages at different intervals.
2. Illustrating isAlive() and join() methods.
3. Daemon Threads.
4. Producer-Consumer problem.

Exercise 8:

1. Importing and using user-defined packages.
2. Creating a JavaFX GUI with text and image (ImageView).
3. Tip Calculator using JavaFX components.

Prepared by: G. Netaji

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2025-26 II-I VR23-OBJECT-ORIENTED PROGRAMMING THROUGH JAVA LAB

netaji gandi Sunday, June 29, 2025

DATA WAREHOUSING AND MINING (MCA2104)

 

UNIT I

Introduction to Data mining, types of Data, Data Quality, Data Processing, Measures of Similarity and  Dissimilarity, Exploring Data: Data Set, Summary Statistics, Visualization, Data Warehouse, OLAP and  multi dimensional data analysis. 

UNIT II

Classification: Basic Concepts, Decision Trees and model evaluation: General approach for solving a classification problem, Decision Tree induction, Model over fitting: due to presence of noise, due to lack of representation samples, Evaluating the performance of classifier. Nearest Neighborhood classifier, Bayesian Classifier, Support vector Machines: Linear SVM, Separable and Non Separable case. 

UNIT III

Association Analysis: Problem Definition, Frequent Item-set generation, rule generation, compact representation of frequent item sets, FP-Growth Algorithms. Handling Categorical, Continuous attributes, Concept hierarchy, Sequential, Sub graph patterns

 

UNIT IV

Clustering: Overview, K-means, Agglomerative Hierarchical clustering, DBSCAN, Cluster evaluation: overview, Unsupervised Cluster Evaluation using cohesion and separation, using proximity matrix, Scalable Clustering algorithm 

 

UNIT V

Web data mining: Introduction, Web terminology and characteristics, Web content mining, Web usage mining, web structure mining, Search Engines: Characteristics, Functionality, Architecture, Ranking of Web Pages, Enterprise search

Download Textbooks

1) Introduction to Data Mining, Tan, Steinbach and Vipin Kumar, Pearson Education, 2016
2) Data Mining: Concepts and Techniques, 2ndEdition, Jiawei Han and Micheline Kamber, ELSEVIER

DATA WAREHOUSING AND MINING (MCA2104)

netaji gandi Sunday, February 9, 2025