The term "cluster analysis" refers to techniques in statistical data analysis used to divide similar data sets into groups or clusters. This technique is employed to identify patterns or structures in data by grouping data points based on their similarity. Cluster analysis is applied in various fields such as data analytics, pattern recognition, and segmentation to gain insights into complex data sets.
Typical software functions in the area of "cluster analysis" include:
Clustering: Automatic grouping of data points based on predefined or statistically determined similarity criteria.
Similarity Measures: Calculation of similarity measures between data points to determine their membership in a cluster.
Visualization: Representation of results through cluster diagrams to visualize the grouping and structure of the data.
Cluster Analysis Algorithms: Implementation of algorithms such as k-means, hierarchical clustering, DBSCAN to perform the analysis.
Interpretation of Results: Analysis and interpretation of clusters to identify patterns, trends, or deviations in the data.
Export and Integration: Export of cluster results for further analysis or integration into other software applications.