Working of Data Mining ⚙️🔄

How does a computer actually "learn" from data? It follows a systematic workflow. You don't just throw data into a machine—you have to prepare it first.

The 4-Step Lifecycle

Loading diagram…

1. Data Collection

This is the gathering phase where raw data is pulled from across the organization.

• Transactional Databases: Sales records, inventory levels, and financial logs.
• Web and Social Media: Customer reviews, Tweets, website clicks, and search history.
• External Market Data: Competitor pricing, stock market trends, and economic reports.
• IoT and Sensor Data: Machine logs from factories or GPS data from delivery trucks.
• Legacy Data: Old paper records that have been digitized for historical analysis.

2. Data Preprocessing (The 80% Rule)

Data scientists spend the majority of their time here because "Dirty Data" leads to "Wrong Insights."

• Data Cleaning: Filling in missing values using averages (Imputation) or removing noise (random errors).
• Data Integration: Merging data from different sources where keys might not match (e.g., matching a "Store ID" with a "Website User ID").
• Data Selection: Choosing only the relevant variables (Features) to reduce the complexity of the mining task.
• Data Transformation: Normalizing data (putting it on a scale of 0 to 1) or consolidating it into summaries (e.g., daily sales instead of individual transactions).
• Data Reduction: Using techniques to represent the same information with less volume, making the mining process faster for Big Data.

3. Pattern Discovery (The Mining Step)

This is the "Black Box" phase where mathematical algorithms search the prepared data for hidden gold.

• Classification & Prediction: Building models to predict labels (e.g., "Will this customer default on a loan?").
• Clustering (Segmentation): Identifying groups of similar items that were not previously known (e.g., discovery of a new "Niche Market").
• Association Rule Discovery: Finding "If-Then" relationships (e.g., "If a customer buys Beer, they are 60% likely to buy Diapers").
• Anomaly (Outlier) Detection: Spotting data points that deviate significantly from the norm, often used for identifying security breaches.
• Regression Analysis: Estimating the relationship between variables to predict a numerical value (e.g., predicting next month's total revenue).

4. Knowledge Representation

The goal is to turn complex math into something a human manager can act upon.

• Advanced Visualization: Using 3D graphs, heatmaps, and interactive dashboards (like Tableau or PowerBI).
• Interpretation & Evaluation: Identifying which of the discovered patterns are actually "Interesting" and useful for the business.
• Reporting: Creating executive summaries that explain the "Wait, so what?" of the data.
• Integration into Strategy: Using the pattern to change business rules (e.g., moving the bread aisle closer to the butter aisle).
• Archiving: Storing the discovered rules for future comparison and trend tracking.

Loading stats…

Summary

1. Collect: Get the raw materials.
2. Preprocess: Clean and format the data (GIGO avoidance).
3. Discovery: Let the algorithms find the "gold."
4. Represent: Present the findings through charts and reports.

Quiz Time! 🎯

Loading quiz…