Objective of Clustering
Clustering is a technique used in machine learning and data science to group similar data points together. The main objective of clustering is to divide a set of unlabeled data points into groups or clusters such that data points within a cluster are more similar to each other than those in other clusters.
Key Objectives of Clustering:
- Finding Natural Groupings: The main goal of clustering is to identify natural groupings or patterns in the data that can provide insights into the underlying structure of the data.
- Data Exploration: Clustering helps in exploring and understanding the relationships between data points and identifying patterns that may not be apparent initially.
- Data Compression: By grouping similar data points into clusters, it is possible to reduce the size of the dataset and simplify the analysis process.
- Outlier Detection: Clustering can help in identifying outliers or anomalies in the data by separating them into their own clusters.
- Improving Classification and Prediction: Clustering can be used as a preprocessing step to improve the performance of classification and prediction models by grouping similar data points together.
Overall, the objective of clustering is to organize and make sense of large complex datasets by grouping similar data points together based on their characteristics and patterns.
