Unit 2: Introduction to data mining
Data Mining for Business Decisions notes · PTU syllabus (MBA 941-18)
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Unit summary
Data mining discovers useful patterns hidden in large data sets. This unit covers the data mining process and functionalities, text, web, pattern, sequence and context-based mining, data visualisation, predictive vs descriptive data mining, and the need for data analytics in business intelligence.
After this unit you can
- Explain the data mining process (KDD and CRISP-DM)
- Explain data mining functionalities
- Explain text, web, sequence and pattern mining
- Distinguish predictive and descriptive mining and explain the role of analytics in BI
PTU syllabus topics
- Data mining process and functionalities
- text/web/pattern/sequence/context-based mining
- data visualization
- predictive vs descriptive data mining
- need for data analytics in business intelligence
- 1
Data selection
- 2
Pre-processing and cleaning
- 3
Transformation
- 4
Data mining
Find patterns
- 5
Interpretation and evaluation
- 6
Knowledge for decisions
Topic 1
Meaning of data mining
Data mining is the process of discovering interesting, non-obvious and useful patterns, relationships and trends in large data sets using statistics, machine learning and database techniques.
- 1
Data cleaning
- 2
Data integration
- 3
Data selection
- 4
Data transformation
- 5
Data mining
Apply algorithms
- 6
Pattern evaluation
- 7
Knowledge presentation
Topic 2
The CRISP-DM process
- 1. Business understanding: Define objectives and success criteria
- 2. Data understanding: Collect, describe, explore data
- 3. Data preparation: Select, clean, construct, integrate, format
- 4. Modelling: Choose and run techniques
- 5. Evaluation: Check results against business goals
- 6. Deployment: Put the model to use and monitor
- About 60–80% of a project's effort typically goes into data understanding and preparation.
Topic 3
Data mining functionalities
Characterisation and discrimination
Summarise a class, compare classes
Association
Items that occur together
Classification
Assign cases to predefined classes
Prediction (regression)
Estimate a numeric value
Clustering
Find natural groups
Outlier (anomaly) detection
Spot unusual cases — fraud
Evolution and trend analysis
Patterns over time
Topic 4
Text, web, pattern, sequence and context-based mining
- Text mining: extracting information from unstructured text — sentiment analysis of reviews, topic modelling, classification of complaints, keyword extraction.
- Web mining: content mining (page contents), structure mining (links between pages — PageRank) and usage mining (clickstreams, logs).
- Pattern mining: frequent itemsets and association rules.
- Sequence mining: ordered patterns over time — customers who buy a phone then buy a cover within a week.
- Context-based mining: patterns that depend on context — time, location, device, weather — used in recommendations and location-based offers.
Example
A telecom firm mines complaint text and finds "network drop" in one district spiking after a tower upgrade — a pattern no structured report showed.
Topic 5
Data visualisation in data mining
- Purpose: explore data before modelling, communicate patterns, evaluate models.
- Tools: scatter plot matrices, heat maps, parallel coordinates, tree diagrams, cluster plots, lift and ROC charts, network graphs.
Topic 6
Predictive vs descriptive data mining
Goal
Predict unknown or future values
Describe patterns in existing data
Learning
Supervised — has a target variable
Unsupervised — no target
Techniques
Classification, regression, time-series prediction
Clustering, association rules, summarisation
Example
Will this customer churn?
What groups of customers exist?
Topic 7
Need for data analytics in business intelligence
- BI reports show what happened; analytics and data mining explain why and predict what will happen, enabling proactive decisions.
- Applications: customer segmentation and targeting, churn prediction, credit scoring, fraud detection, market basket analysis, demand forecasting, predictive maintenance, HR attrition analysis.
- Challenges: data quality, privacy (DPDP Act 2023), skills, model bias, explaining results to managers.
Key terms
- Data mining
- Discovering useful patterns in large data sets
- KDD
- Knowledge discovery in databases
- CRISP-DM
- Six-phase standard process for data mining
- Web usage mining
- Mining clickstream and log data
- Supervised learning
- Learning with a known target variable
Quick revision
- KDD steps; CRISP-DM six phases.
- Functionalities: characterisation, association, classification, prediction, clustering, outliers, trends.
- Text, web (content, structure, usage), sequence and context mining.
- Visualisation for exploration and evaluation.
- Predictive (supervised) vs descriptive (unsupervised); analytics in BI.
Important exam questions
Practice questions written to the PTU exam pattern for this unit's syllabus: short answers (Section A style) and long answers (Sections B and C style).
Short-answer questions
- Q1.Define data mining.
- Q2.Name the phases of CRISP-DM.
- Q3.What is text mining?
- Q4.Name the three types of web mining.
- Q5.Distinguish predictive and descriptive data mining.
- Q6.Give two business applications of data mining.
Long-answer questions
- Q1.Explain the data mining process using KDD and CRISP-DM.
- Q2.Explain the functionalities of data mining.
- Q3.Discuss text, web and sequence mining with examples.
- Q4.Explain the need for data analytics in business intelligence.
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