DCA2207 DATA MINING AND VISUALIZATION JAN FEB 2026

Description

SESSION	JANUARY/FEBRUARY 2026
PROGRAM	BACHELOR OF COMPUTER APPLICATIONS (BCA)
SEMESTER	IV
COURSE CODE & NAME	DCA2207  DATA MINING AND VISUALIZATION

 

SET-I

 

Q.1. Define Data Mining. Explain its importance in the modern data-driven world. (5+5 = 10 Marks)

Ans 1.

Definition of Data Mining

Data Mining refers to the process of automatically discovering relevant patterns, relationships, abnormalities, and valuable insights hidden within large datasets using statistical, mathematical, and machine-learning techniques. This is also known in the form of Knowledge Discovery in Databases (KDD). The raw data stored in databases, data warehouses and other repositories of information is processed through a structured process that involves data cleansing Data

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Q.2. Discuss the difference between Operational Databases and Data Warehouse. (5+5 = 10 Marks)

Ans 2.

Operational Databases (OLTP)

Operational databases, often referred to Online Transaction Processing (OLTP) Systems, are made to support the routine day-to-day business transactions. They handle extremely large volumes of short, real-time transactions which include insertions, changes, deletions, and simple lookup inquiries. The information stored in operational databases reflects what is happening in the operational processes and can be updated constantly and in real-time. These databases use highly normalized schema designs to eliminate data redundancy and maintain the integrity of referential data, which ensures continuity across many concurrent transactions. For instance, a

 

 

Q.3. What is a Data Cube? Describe how data cubes organize data into dimensions and measures to support fast analytical queries. (3+7 = 10 Marks)

Ans 3.

Definition of a Data Cube

A Data Cube is a multi-dimensional data structure used for Online Analytical Processing (OLAP) systems for arranging business information across multiple categorical viewpoints called dimensions, enabling analysts to look at slices, aggregate, and view data from multiple angles at once. As opposed to tables that are flat and two-dimensional that are slender, data cubes can have three or more dimensions that provide a deeper and better-organized representation of the complex business information. Each cell in the cube has a measurement value corresponding

 

SET-II

 

Q.4. What is an Association Rule? Define Support, Confidence, and Lift. Explain their importance in Association Rule Mining. (3+7 = 10 Marks)

Ans 4.

Association Rule

An Association Rule is an if-then implied statement in the form of A implies B that is automatically derived from huge transactional datasets, expressing that transactions which contain item A are highly likely to also contain itemset B. Association rule mining is the computation process that identifies every relevant rule from transaction databases.

Support

Support describes how often the rule or item occurs across the entire transaction database.

 

Q.5. Discuss how charts convert raw data into visual information for better understanding and decision-making. (10 Marks)

Ans 5.

Role of Charts in Data Visualization

Charts are visual representations of data that transform raw numerical data into visual formats so that our brains are able to process, interpret, and remember more efficiently as tables of data. The research in cognitive psychology shows that human brains process visual data 60 times faster than numeric or textual data. An effective chart immediately reveals trends, patterns, comparative relationships, distributions, and other outliers that require hours of research to uncover from data tables. Charts translate the meaning of numbers into the visual words of sizes,

 

Q.6. Explain the key steps of the dashboard development process in detail. (10 Marks)

Ans 6.

Introduction to Dashboard Development

Dashboards provide an interactive interface for visuals which consolidates and presents key measures of performance, indicators and trends from a variety of data sources on one screen, enabling stakeholders to monitor organizational health and make informed choices in a single glance. A dashboard that actually serves those who need it needs a systematic, user-centered approach that is able to balance technical implementation in conjunction with design principles