Klusterointilähestymistavat

Klusterointilä, also known as clustering, is an unsupervised machine learning technique used to group a set of objects in such a way that objects in the same group, called a cluster, are more similar to each other than to those in other groups. This similarity is typically defined by a distance metric, where data points closer in the feature space are considered more alike. The primary goal of clustering is to discover inherent groupings or structures within data without prior knowledge of those groupings.

There are various algorithms for performing clustering, each with its own approach and assumptions. K-means is a popular partitioning method that aims to partition n observations into k clusters, where each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. Hierarchical clustering, on the other hand, creates a tree of clusters, either by agglomerating smaller clusters into larger ones (agglomerative) or by recursively partitioning larger clusters (divisive). Density-based clustering algorithms, such as DBSCAN, identify clusters as dense regions of data points separated by sparser regions.

Clustering finds applications in a wide range of fields. In marketing, it's used for customer segmentation to tailor marketing campaigns. In biology, it can be applied to group genes with similar expression patterns or to classify species. Image segmentation, anomaly detection, and document analysis are other areas where clustering plays a crucial role. The choice of clustering algorithm and the interpretation of the resulting clusters often depend heavily on the specific problem and the nature of the data.