Hierarchical Clustering: Builds

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Technographic: Devices used, software preferences. This data then needs to be cleaned, pre-processed (handling missing values, outliers), and often transformed to be suitable for algorithmic analysis.
Algorithm Selection: Choosing the right algorithm depends on the nature of the data, the business objectives, and whether existing segments are already defined. Key categories include:

Clustering Algorithms (Unsupervised Learning): These are country email list perhaps the most common for customer segmentation, as they automatically discover inherent groupings within the data without prior knowledge of what those groups should be.
K-Means Clustering: A popular and relatively simple algorithm that partitions data points into 'K' distinct clusters. It iteratively assigns each data point to the cluster whose centroid (mean) is closest, then recalculates the centroids until convergence. The "elbow method" is often used to determine the optimal 'K' value.


a hierarchy of clusters, either by starting with individual data points and merging them (agglomerative) or starting with one large cluster and dividing it (divisive). This results in a tree-like structure (dendrogram) that visualizes relationships between clusters.
DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Identifies clusters based on the density of data points, effectively finding arbitrary-shaped clusters and identifying outliers (noise). This is useful when clusters are not necessarily spherical.
Gaussian Mixture Models (GMM): A probabilistic model that assumes data points within a cluster are generated from a Gaussian distribution. GMMs are more flexible than K-Means, capable of handling overlapping clusters and those with varying shapes.
Self-Organizing Maps (SOMs): A type of neural network that maps high-dimensional data onto a lower-dimensional grid, preserving the topological relationships of the input data. Similar customers are located closer on the map.
Classification Algorithms (Supervised Learning): Used when businesses have predefined customer segments and want to classify new customers into these existing groups. These algorithms learn from labeled data.
Decision Trees: Creates a tree-like model of decisions and their possible consequences, where each branch represents a choice and each leaf represents a classification.