Data Mining - Practical Machine Learning Tools and Techniques

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.