Clustering is an unsupervised machine learning technique that groups together comparable data items based on their intrinsic properties or patterns. Clustering seeks to uncover underlying structures within a dataset that lacks predetermined labels or classifications. The following is how clustering works:
1. Data Preparation: Begin by creating a dataset with a collection of data points. These information points might be anything from customer profiles to genetic sequences.
2. Similarity Measurement: Define a metric for comparing the similarity or dissimilarity of data points. Euclidean distance, cosine similarity, and other domain-specific distance functions are common metrics.
3. Clustering Algorithm: Select a clustering algorithm to divide the data into clusters. K-Means, Hierarchical Clustering, DBSCAN, and many other methods are common.
4. Clustering Process: The selected algorithm assigns data points to clusters iteratively based on their similarity and changes the cluster centroids or structures until a stopping criterion is satisfied. The number of clusters might be set (as in K-Means) or automatically determined (as in DBSCAN).
5. Interpretation: Once the clustering is complete, the findings can be interpreted by analysing the properties of the data points inside each cluster. This analysis aids in comprehending the data’s underlying patterns or structures.
Clustering has numerous applications in a variety of fields. It is used in consumer segmentation for targeted marketing, image processing to group comparable images, biological data analysis for gene expression pattern identification, and a variety of other industries where uncovering hidden patterns or correlations inside data is important.