Data Mining concepts

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Across
  1. 3. Improving patient care by identifying risk factors and potential treatment outcomes.
  2. 5. Aspects of Data Mining
  3. 7. Detecting fraudulent transactions and credit card fraud.
  4. 8. Data is gathered, cleansed (scrubbed for outliers), and then analyzed for patterns.
Down
  1. 1. Common methods include classification (classifying data into groups), clustering (grouping similar data points), regression (predicting numerical values), and association rule mining (finding relationships between variables).
  2. 2. Analyzing customer behavior to personalize experiences and target campaigns.
  3. 4. Optimizing production processes and reducing equipment downtime.
  4. 6. Increased revenue, improved operational efficiency, better risk management, and strengthened competitive advantage.