A type of machine learning where the model learns patterns from unlabeled data without explicit supervision.

A technique in unsupervised learning that groups data points based on similarity, facilitating pattern recognition.

A popular clustering algorithm that partitions data into K clusters by minimizing variance within each cluster.

A clustering method that builds a hierarchy of clusters, either agglomeratively or divisively.

A metric used to evaluate clustering quality, measuring how similar an object is to its own cluster compared to others.

The process of dividing a customer base into distinct groups for targeted marketing strategies.

Techniques like normalization or standardization used to adjust the range of data features for effective clustering.

A technique to determine the optimal number of clusters in K-Means by plotting the explained variance against the number of clusters.

The steps taken to clean and prepare raw data for analysis, ensuring high-quality input for clustering.

The underlying mathematical concepts that support clustering algorithms, such as distance metrics.

An assessment of the condition of data based on accuracy, completeness, reliability, and relevance.

Data types that represent categories or groups, often requiring transformation for clustering analysis.

The practice of using graphical representations to illustrate how data points are grouped in clusters.

A method of evaluating and contrasting different clustering techniques to identify strengths and weaknesses.

Marketing approaches tailored to specific customer segments based on data insights from clustering.

The process of recording and detailing methods, findings, and insights throughout the clustering project.

The ability to convey complex clustering results to non-technical stakeholders in an understandable manner.

A bottom-up approach to hierarchical clustering where each data point starts as its own cluster.

A top-down approach to hierarchical clustering that starts with one cluster and splits it into smaller ones.

Valuable information derived from data analysis that informs marketing strategies and decisions.

A statistical measure used to evaluate how well the clustering model explains the variance in the data.

Methods used to create visual representations of data, aiding in the interpretation of clustering results.

The process of refining marketing strategies based on ongoing analysis and feedback from clustering results.

The involvement of relevant parties in discussions about clustering findings and marketing strategies.