Data Mining: Process, Methods, Algorithms - ICT370 Data Analytics

This presentation provides a comprehensive overview of data mining, covering its definition, objectives, benefits, and various applications across different industries such as customer relationship management, finance, retail, and healthcare. It delves into the standardized data mining processes, including CRISP-DM, SEMMA, and KDD, emphasizing the iterative and experimental nature of data mining projects. The presentation also explores different data mining methods and algorithms like classification, cluster analysis, and association rule mining, along with assessment methodologies and classification techniques. Furthermore, it introduces various data mining software tools, discusses the privacy issues, pitfalls, and myths associated with data mining, and concludes with a summary of key concepts. The presentation draws from the lecture notes of ICT370 Data Analytics course.

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

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
2. Understand the objectives and benefits of data mining
3. Become familiar with the wide range of applications of dat
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 m

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 discover
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

Data Mining Applications (2 of 4)
• Retailing and Logistics
– Optimize inventory levels at different locations
– Improve the store layout and sales promotions
– Optimize logistics by predicting seasonal effects
– Minimize losses due to limited shelf life
• Manufacturing and Maintenance
– Predict/prevent machinery failures
– Identify anomalies in production systems to optimize the use manufactu
capacity
– Discover novel patterns to improve product quality

Data Mining Applications (3 of 4)
• Brokerage and Securities Trading
– Predict changes on certain bond prices
– Forecast the direction of stock fluctuations
– Assess the effect of events on market movements
– Identify and prevent fraudulent activities in trading
• Insurance
– Forecast claim costs for better business planning
– Determine optimal rate plans
– Optimize marketing to specific customers
– Identify and prevent fraudulent claim activities

Data Mining Applications (4 of 4) – Examples?
• Computer hardware and software
• Science and engineering
• Government and defense
• Homeland security and law enforcement
• Travel, entertainment, sports
• Healthcare and medicine
• Sports,… virtually everywhere…

Data Mining Process
• A manifestation of the best practices
• A systematic way to conduct DM projects
• Moving from Art to Science for DM project
• Most common standard processes:
– CRISP-DM (Cross-Industry Standard Process for Data Mining)
– SEMMA (Sample, Explore, Modify, Model, and Assess)
– KDD (Knowledge Discovery in Databases)