Data Mining Techniques 🧬⛏️
Data mining isn't a single magic button. It is a toolbox filled with different techniques. These techniques broadly fall into two groups: those that Describe the present and those that Predict the future.
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1. Descriptive Techniques (Discovery & Summary)
Descriptive techniques categorize and summarize data to find human-understandable patterns that already exist in the database.
- Clustering (Customer Segmentation): Automatically grouping thousands of customers into "Clusters" based on similar age, income, and spending habits to build better marketing campaigns.
- Association Rule Mining (Market Basket Analysis): Discovering items that are frequently bought together (e.g., "70% of people who buy a laptop also buy a mouse and a padded case").
- Data Summarization & Profiling: Using statistical measures like Mean, Mode, and Standard Deviation to build a high-level "Profile" of a typical customer or a typical productive day.
- Sequence Discovery: Identifying patterns where one event leads to another over time (e.g., "Customers who buy a Printer usually buy their first Ink refill within 45 days").
- Outlier Detection (Anomaly Mining): Focusing on data points that don't fit the pattern, which is crucial for identifying bank fraud or manufacturing defects.
2. Predictive Techniques (Forecasting & Labels)
Predictive techniques use historical data to build a mathematical "Model" that can guess the outcome of future events.
- Classification (Decision Grouping): Assigning a "Label" to a new data point based on past examples (e.g., "Based on past behavior, this new loan applicant is a 'High Risk' borrower").
- Regression (Numerical Prediction): Predicting a continuous numerical value rather than a label (e.g., "Given current trends, the price of a house in this area will be $450,000 in six months").
- Time Series Forecasting: Analyzing data points collected at regular intervals to predict future seasonal trends (e.g., "Predicting the 200% spike in ice cream sales during the upcoming summer months").
- Decision Tree Modeling: Building a "Tree" of questions (e.g., "Is age > 18?" -> "Is income > $50k?") to reach a final predictive conclusion in a way that humans can easily explain.
- Propensity Modeling: Predicting the "Likelihood" of a specific action (e.g., "What is the 80% probability that this customer will click on 'Unsubscribe' next month?").
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Summary
- Descriptive data mining looks for human-understandable patterns.
- Predictive data mining aims to accurately forecast future labels or values.
- Both are necessary for a complete Business Intelligence system.
Quiz Time! 🎯
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