What is meant by Clustering?
"Clustering" refers to the grouping of data or objects into subsets (clusters), where each cluster exhibits similar characteristics and differs from other clusters. This method is used to identify natural groupings or patterns in large datasets without prior knowledge of the exact assignment of data to specific categories.
Typical software functions in the area of "clustering":
- Cluster analysis: Identification of groups of similar data points based on statistical or algorithmic methods.
- Visualization: Graphical representation of cluster structures for easier interpretation.
- Parametric and non-parametric methods: Application of various clustering algorithms depending on data type and application.
- Feature selection: Selection of relevant features for clustering.
- Automated clustering: Algorithms that can automatically identify and create clusters in data.
- Cluster validation: Evaluation of the quality of clustering and its relevance for analysis.
- Integration with analysis tools: Linkage with other analysis tools for further evaluation of clustering results.
Examples of "clustering":
- Customer segmentation: Division of customers into groups based on their purchasing behavior and preferences.
- Medical diagnosis: Classification of patient data into groups with similar symptoms to support diagnosis.
- Market research: Identification of market segments with similar attitudes and behaviors.
- Image processing: Grouping similar image regions for object recognition and segmentation.
- Anomaly detection: Identification of outliers or unusual patterns in data.