Cluster analysis is a data mining technique used to group similar data points into clusters or categories based on shared characteristics or patterns. In Business Intelligence (BI), it helps organizations identify natural groupings in their data to make better decisions.
Definition
Cluster analysis is a method to divide data into homogeneous groups (clusters) where objects in the same cluster are more similar to each other than to objects in other clusters. It is an unsupervised learning technique widely used for segmentation and pattern discovery.
How it Supports Organizational Efficiency
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Customer Segmentation
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Helps in grouping customers based on buying behavior, demographics, or preferences.
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Example: Identifying high-value customers for targeted marketing.
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Product Categorization
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Groups products based on features or performance metrics.
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Example: Organizing inventory for optimized sales.
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Market Analysis
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Segments geographical regions based on sales potential or customer density.
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Example: Allocating resources efficiently to high-potential areas.
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Fraud Detection
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Identifies unusual patterns or anomalies that may indicate fraudulent activity.
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Example: Clustering credit card transactions to detect outliers.
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Operational Optimization
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Groups similar operational tasks to streamline workflows and reduce costs.
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Example: Optimizing delivery routes by clustering delivery points.
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Improved Decision-Making
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Offers actionable insights by identifying hidden patterns in data.
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Diagram of Cluster Analysis
A simple visual of cluster analysis is a scatter plot where data points are grouped into clusters. Let me generate a clear diagram for better understanding.
