Klusterointimenetelmä

Klusterointimenetelmä, known in English as clustering, is a machine learning technique used to group data points such that those in the same group (called a cluster) are more similar to each other than to those in other groups. It is an unsupervised learning method, meaning it does not require pre-labeled data. The goal of clustering is to discover inherent groupings within a dataset.

Various algorithms exist for performing clustering, each with its own approach to defining similarity and forming clusters. A common method is k-means clustering, which aims to partition data into k predefined clusters. The algorithm iteratively assigns data points to the nearest cluster centroid and then updates the centroid based on the mean of the assigned points. Another popular technique is hierarchical clustering, which builds a tree of clusters. This can be done in a bottom-up (agglomerative) or top-down (divisive) manner. Density-based clustering, such as DBSCAN, identifies clusters based on regions of high density separated by regions of low density, and can discover arbitrarily shaped clusters.

The choice of clustering algorithm depends on the nature of the data and the desired outcome. Similarity is often measured using distance metrics like Euclidean distance, Manhattan distance, or cosine similarity. Applications of clustering are widespread, including customer segmentation in marketing, anomaly detection in cybersecurity, image segmentation in computer vision, and biological data analysis. The effectiveness of a clustering method is typically evaluated by internal metrics that assess the quality of the clusters formed without external information, or external metrics that compare the clusters to known class labels.