klastreerimist
Klastreerimist, often translated as clustering, is a fundamental technique in machine learning and data analysis. Its primary goal is to partition a set of data points into groups, or clusters, such that data points within the same cluster are more similar to each other than to those in other clusters. This similarity is typically defined by a distance metric, such as Euclidean distance, which measures how close two data points are in a multi-dimensional space.
The process of klastreerimist involves identifying inherent structures or patterns within unlabeled data. Unlabeled data means
There are numerous klastreerimist algorithms, each with its own strengths and weaknesses. Some popular methods include
The applications of klastreerimist are diverse and span many fields. In marketing, it's used for customer segmentation