MITS4003 Database Systems Assignment: Data Mining Presentation Report

This presentation provides an overview of data mining, defining it as the process of extracting useful and hidden analytical information from data, also known as Knowledge Discovery in Databases (KDD). It highlights the uses of data mining in determining practices, reducing costs, providing meaningful patterns, and converting data into information through multi-dimensional approaches like machine learning, soft computing, data visualization, and statistics. The presentation outlines the steps associated with KDD, including data cleaning, integration, selection, transformation, data mining, pattern evaluation, and knowledge representation. It further classifies data mining into anomaly detection, association rule learning, clustering, classification, regression, and summarization. The presentation also touches upon the applications of data mining in market analysis, corporate analysis, risk management, and fraud detection. References to relevant academic papers and books are provided to support the content.

DATA MINING – PRACTICAL MACHINE
LEARNING TOOLS AND TECHNIQUES
Author of the chosen paper
(Ian H. Written, Eibe Frank, Mark A. Hall & Christopher J. Pal)

What is data mining?
• The process of extracting
useful information
• Process of extracting hidden
analytical information
• Helps in retrieving data from
available data warehouse
• This is also known as KDD
( Knowledge Discovery in
Databases)

Uses of data mining
• Uses data to determine the practices
• Helps in reducing cost
• Provides meaningful patterns
• Helps in converting data into information
• data mining uses multi dimensional such as, machine learning, soft
computing, data visualization and statistics

Components of data mining

Knowledge Discover in Databases
(KDD)
• Steps associated with KDD are:
• Data cleaning
• Data integration
• Data selection
• Data transformation
• Data mining
• Pattern evaluation
• Knowledge representation

Classes of data mining
• Anomaly detection: helps in detecting the
errors identified within the records.
• Association rule learning: identifies the
relationship within the variables.
• Clustering: identifies the similar data
• Classification: this includes application of
new data structure
• Regression: this includes identifying data sets
with least error
• Summarization: generates more compact
representation

Applications of data mining
• Market analysis and
management
• Corporate analysis and
risk management
• Fraud detection

References
• Cao, L. (2015). Actionable knowledge discovery and delivery. In
Metasynthetic computing and engineering of complex systems (pp.
287-312). Springer, London.
• Dua, S., & Du, X. (2016). Data mining and machine learning in
cybersecurity. Auerbach Publications.
• Gamarra, C., Guerrero, J. M., & Montero, E. (2016). A knowledge
discovery in databases approach for industrial microgrid planning.
Renewable and Sustainable Energy Reviews, 60, 615-630.