Cluster Analysis
A statistical method for identifying spatial groupings or concentrations of features, widely used in spatial pattern analysis.
How do you define Cluster Analysis?
A statistical and geographical technique called cluster analysis is used to organize related data points or features into clusters according to common traits or trends. It aids in the identification of geographic clusters or hotspots in GIS and spatial analysis where specific phenomena, like disease outbreaks, criminal activity, or population density, are concentrated.
Cluster analysis's objective is to make sure that:
Clusters of features are more similar to one another,
Additionally, the characteristics of the various clusters differ considerably from one another.
In order to comprehend spatial linkages, trends, and patterns in sizable datasets, it is extensively utilized in disciplines such as epidemiology, market research, urban planning, and environmental studies. Through the identification of areas of interest or concern, cluster analysis facilitates better decision-making.
Related Keywords
Hierarchical Cluster Analysis (HCA) shows the gradual merging of objects by grouping them into clusters and displaying them in a dendrogram. It is used to identify patterns without predetermined cluster numbers in domains like as marketing, biology, and the social sciences.
K-Means An unsupervised process called clustering divides data points into groups according to how similar they are. It helps identify patterns in data by iteratively allocating points to the closest cluster centre and updating centres to reduce within-cluster distance.
Similar data points are grouped via data clustering according to features or patterns. K-Means, Hierarchical Clustering, and DBSCAN are examples of common methods. It supports choices in domains like image analysis, biology, and marketing by illuminating trends and streamlining enormous datasets.
In data mining, cluster analysis reveals hidden patterns and correlations by grouping comparable data points into clusters. It is extensively utilized in anomaly detection, consumer analysis, and market segmentation.