Partitioning
Validation
Concepts
Key Considerations for BI Architecture
Evaluating Predictive Analytics Performance
Data Mining Techniques for Policy Analysis
Data Mining Techniques for Social Network Analysis
Assessing Predictive Analytics Performance Metrics
Data Scalability 
Data
Partitioning Partitioning data into smaller, manageable chunks can help improve performance and scalability
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Data Optimization Data
Partitioning Data partitioning divides a database into smaller, more manageable pieces, which can improve query performance and data management
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Validation Cross-Validation: A technique that involves
partitioning the data into subsets and testing the model multiple times
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Concepts K-Means Clustering
Partitioning method to classify data into k groups
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Key Considerations for BI Architecture To optimize performance, organizations can: Use efficient data storage solutions Implement data indexing and
partitioning Utilize caching mechanisms to speed up data retrieval 2
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Evaluating Predictive Analytics Performance Cross-Validation Cross-validation involves
partitioning the dataset into multiple subsets, training the model on some subsets (the training set) and validating it on the remaining subsets (the validation set)
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Data Mining Techniques for Policy Analysis K-Means Clustering: A popular method for
partitioning data into K distinct clusters
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Data Mining Techniques for Social Network Analysis algorithms used for clustering include: Algorithm Description K-Means A
partitioning method that divides nodes into K clusters based on distance metrics
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Assessing Predictive Analytics Performance Metrics Common techniques include: Cross-Validation: A technique that involves
partitioning the data into subsets, training the model on some subsets and validating it on others to ensure that the model generalizes well
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Measuring Effectiveness of Predictive Models 2 Cross-Validation Cross-validation is a more robust method that involves
partitioning the data into multiple subsets
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