përklasterimin

Përklasterimi refers to the process of grouping similar data points together into clusters. This is a fundamental technique in unsupervised machine learning, meaning it does not require pre-labeled data. The goal is to discover inherent patterns and structures within the dataset by identifying groups of objects that are more similar to each other than to those in other groups.

Several algorithms exist for performing përklasterimi, each with its own strengths and weaknesses. K-means is a popular centroid-based algorithm where data points are assigned to the cluster with the nearest mean (cluster centroid). Hierarchical clustering, on the other hand, builds a hierarchy of clusters, either by progressively merging smaller clusters or by recursively splitting larger ones. Density-based clustering algorithms, such as DBSCAN, identify clusters as dense regions of data points separated by sparser regions.

The choice of algorithm and the number of clusters (if applicable, as in k-means) often depend on the nature of the data and the specific problem being addressed. Përklasterimi has a wide range of applications, including customer segmentation for marketing, image segmentation, anomaly detection, document analysis, and biological data analysis. It helps in understanding complex datasets by revealing underlying groupings that might not be immediately obvious.