ICT370 Data Analytics PDF

CRICOS 03171A
ICT370
Data Analytics
Lecture 6 – Data Mining Process, Methods, and Algorithms
Slides Adopted from Sharda, Ramesh, et al. Business Intelligence: A Managerial Approach, Global Edition, Pearson Education Limited, 2017.

Last Week Summary
• Understand data warehousing concepts and architectures
• Describe the processes used in developing and managing
data warehouses and data warehousing operations
• Explain the role of data warehouses in decision support
• Explain data integration and the extraction, transformation,
and load (ETL) processes
• DW Development considerations

CRICOS 03171A
Learning Objectives
1. Define data mining as an enabling technology for business analytics
2. Understand the objectives and benefits of data mining
3. Become familiar with the wide range of applications of data mining
4. Learn the standardized data mining processes
5. Learn different methods and algorithms of data mining
6. Build awareness of the existing data mining software tools
7. Understand the privacy issues, pitfalls, and myths of data mining

OPENING VIGNETTE Miami-Dade Police
Department Is Using Predictive Analytics
to Foresee and Fight Crime (1 of 3)
• Predictive analytics in law enforcement
– Policing with less
– New thinking on cold cases
– The big picture starts small
– Success brings credibility
– Just for the facts
– Safer streets for smarter cities

Definition of Data Mining
• The nontrivial process of identifying valid, novel, potentially useful, and
ultimately understandable patterns in data stored in structured databases.
-- Fayyad et al., (1996)
• Keywords in this definition: Process, nontrivial, valid, novel, potentially
useful, understandable.
• Data mining: a misnomer?
• Other names: knowledge extraction, pattern analysis, knowledge discovery,
information harvesting, pattern searching, data dredging,...
• Why we need Data mining-- Predictive Analytics

Data Mining Is a Blend of Multiple Disciplines
• FIGURE 4.1
Data Mining Is a
Blend of Multiple
Disciplines

How Data Mining Works
• DM extract patterns from data
– Pattern? A mathematical (numeric and/or
symbolic) relationship among data items
– Build models based on these patterns
• Types of patterns
– Association
– Prediction
– Cluster (segmentation)
– Sequential (or time series) relationships

Data Mining Applications (1 of 4)
• Customer Relationship Management
– Maximize return on marketing campaigns
– Improve customer retention (churn analysis)
– Maximize customer value (cross-, up-selling)
– Identify and treat most valued customers
• Banking & Other Financial
– Automate the loan application process
– Detecting fraudulent transactions
– Maximize customer value (cross-, up-selling)
– Optimizing cash reserves with forecasting