, FOR3705 Assignment 1 (COMPLETE ANSWERS)
Semester 1 2026 - DUE March 2026; 100% Correct solutions
and explanations.
Question 1
Explain what is meant by ‘data mining’ and discuss how data
mining tools are used to identify patterns and indicators of fraud in
large datasets.
Answer:
Definition of Data Mining:
Data mining is the process of analyzing large sets of data to discover
patterns, trends, correlations, and useful information that may not be
immediately obvious. It involves using statistical, mathematical, and
computational techniques to extract meaningful insights from raw data.
The goal of data mining is to transform data into knowledge that can
support decision-making, predictions, and problem-solving.
Data mining is not just about collecting data; it is about uncovering
hidden patterns, relationships, or anomalies in data that could provide
strategic insights for businesses, government agencies, or researchers. In
essence, it is a method of "digging through" massive datasets to find
valuable information.
Use of Data Mining in Fraud Detection:
Fraud detection is one of the key applications of data mining.
Organizations, especially banks, insurance companies, and e-commerce
platforms, deal with massive volumes of transactions daily. Detecting
fraudulent activity manually is nearly impossible due to the sheer size
and complexity of the data. Data mining tools help by automatically
analyzing these datasets to detect unusual patterns or behaviors that may
indicate fraud.
How Data Mining Tools Identify Patterns and Indicators of Fraud:
Semester 1 2026 - DUE March 2026; 100% Correct solutions
and explanations.
Question 1
Explain what is meant by ‘data mining’ and discuss how data
mining tools are used to identify patterns and indicators of fraud in
large datasets.
Answer:
Definition of Data Mining:
Data mining is the process of analyzing large sets of data to discover
patterns, trends, correlations, and useful information that may not be
immediately obvious. It involves using statistical, mathematical, and
computational techniques to extract meaningful insights from raw data.
The goal of data mining is to transform data into knowledge that can
support decision-making, predictions, and problem-solving.
Data mining is not just about collecting data; it is about uncovering
hidden patterns, relationships, or anomalies in data that could provide
strategic insights for businesses, government agencies, or researchers. In
essence, it is a method of "digging through" massive datasets to find
valuable information.
Use of Data Mining in Fraud Detection:
Fraud detection is one of the key applications of data mining.
Organizations, especially banks, insurance companies, and e-commerce
platforms, deal with massive volumes of transactions daily. Detecting
fraudulent activity manually is nearly impossible due to the sheer size
and complexity of the data. Data mining tools help by automatically
analyzing these datasets to detect unusual patterns or behaviors that may
indicate fraud.
How Data Mining Tools Identify Patterns and Indicators of Fraud:
