Internal validation measures the cohesion and separation of the clusters based on the data itself, without using any external information or labels. Some common internal validation indices are the silhouette coefficient, the Calinski-Harabasz index, and the Davies-Bouldin index. These indices compare the average distance or similarity within each cluster to the average distance or similarity between clusters, and assign a score that reflects how well the clusters are separated and compact. You can use internal validation to compare different clustering algorithms, parameters, or number of clusters, and choose the one that maximizes the score.
External validation measures the agreement between the clusters and some external reference or ground truth, such as predefined classes or labels. Some common external validation indices are the Rand index, the adjusted Rand index, and the normalized mutual information. These indices compare the pairs of objects that are in the same or different clusters to the pairs of objects that are in the same or different classes, and assign a score that reflects how well the clusters match the classes. You can use external validation to assess how well your clustering results reflect the underlying structure or meaning of the data.
Stability validation measures the robustness and consistency of the clusters across different subsets or perturbations of the data. Some common stability validation methods are the bootstrap, the cross-validation, and the subsampling. These methods generate multiple samples of the data by resampling, splitting, or reducing the original data, and apply the same clustering algorithm to each sample. Then, they compare the clusters obtained from different samples using some similarity or distance measure, and assign a score that reflects how stable the clusters are. You can use stability validation to check if your clustering results are sensitive to noise, outliers, or missing values.
Text data consists of words, sentences, documents, or other units of natural language. To apply cluster analysis validation to text data, you need to first transform the text into numerical features that capture its meaning or structure. Some common text feature extraction methods are the bag-of-words, the term frequency-inverse document frequency (TF-IDF), and the word embeddings. Then, you can use any of the validation methods mentioned above to evaluate your text clusters. However, you should be aware of some challenges and limitations of text clustering validation, such as the high dimensionality, the sparsity, the ambiguity, and the subjectivity of text data.
Image data consists of pixels, regions, shapes, or other units of visual information. To apply cluster analysis validation to image data, you need to first extract relevant features that describe the image content or appearance. Some common image feature extraction methods are the color histograms, the edge detectors, the local binary patterns, and the convolutional neural networks. Then, you can use any of the validation methods mentioned above to evaluate your image clusters. However, you should be aware of some challenges and limitations of image clustering validation, such as the variability, the complexity, the diversity, and the context-dependence of image data.
Network data consists of nodes, edges, attributes, or other units of relational information. To apply cluster analysis validation to network data, you need to first define a suitable measure of similarity or distance between nodes based on their attributes, connections, or positions. Some common network similarity or distance measures are the cosine similarity, the Euclidean distance, the Jaccard coefficient, and the shortest path length. Then, you can use any of the validation methods mentioned above to evaluate your network clusters. However, you should be aware of some challenges and limitations of network clustering validation, such as the sparsity, the heterogeneity, the dynamics, and the scalability of network data.