Klastrianalüüsi

Klastrianalüüsi, known in English as cluster analysis, is a statistical method 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. It is an exploratory data analysis technique and is not a supervised learning algorithm, meaning it does not require pre-defined categories. The goal is to discover inherent groupings within the data.

The similarity or dissimilarity between objects is typically measured using a distance metric, such as Euclidean distance or Manhattan distance. The choice of distance metric depends on the nature of the data. Klastrianalüüsi can be broadly categorized into two main types: hierarchical and non-hierarchical (or partitional). Hierarchical clustering builds a tree of clusters, either by starting with each object as its own cluster and merging them (agglomerative) or by starting with one large cluster and splitting it (divisive). Non-hierarchical methods, such as k-means clustering, partition the data into a pre-determined number of clusters.

Applications of klastrianalüüsi are widespread across various fields. In marketing, it is used for customer segmentation to tailor marketing strategies. In biology, it helps in classifying genes or proteins with similar functions. In image analysis, it can be used for image segmentation. It is also employed in document clustering, anomaly detection, and social network analysis. The effectiveness of klastrianalüüsi often depends on the quality of the data and the appropriate selection of the clustering algorithm and parameters.