The term "distance matrix" refers to a tabular representation that quantifies the distances or similarities between multiple objects or data points. Typically, it is a square matrix where the rows and columns represent the same set of objects. Distance matrices are essential in fields such as cluster analysis, machine learning, genomics, logistics, or recommendation system design. They serve as the basis for many algorithms dealing with grouping, classification, or optimization.
Calculation of various distance metrics: Support for metrics such as Euclidean, Manhattan, Minkowski, or cosine distance.
Visualization of the distance matrix: Display of the matrix as a heatmap or diagram for better analysis of distance relationships.
Normalization and scaling: Data preprocessing through standardization to ensure correct distance computation.
Integration with clustering algorithms: Use of the matrix in methods such as k-means, DBSCAN, or hierarchical clustering.
Data import and export: Support for common file formats like CSV, JSON, or Excel for matrix exchange.
Calculation based on categorical or numerical data: Differentiated calculation logic depending on data types.
Storage and comparison of distance matrices: Versioning and analysis of historical distance calculations.
A bioinformatics tool generates a distance matrix to identify genetic similarities between DNA sequences.
A logistics software computes a distance matrix between warehouses and delivery destinations to optimize route planning.
A recommendation system uses distance matrices to analyze similarities between users or products.
A data science platform visualizes a distance matrix to identify cluster structures within a dataset.
A machine learning module uses distance matrices as input for classification models.