Clustering §

Group: 4 #group-4

Relations §

• Feature Extraction: Clustering can be used for feature extraction by identifying the most representative features of each cluster.
• Dimensionality Reduction: Clustering can be used as a dimensionality reduction technique by representing data points with their cluster assignments.
• Pattern Recognition: Clustering is used in pattern recognition to identify groups or patterns in data.
• Image Segmentation: Clustering algorithms are used in image segmentation to group pixels based on their similarity, separating objects from the background.
• Customer Segmentation: Clustering is used in customer segmentation to group customers based on their behavior or characteristics.
• Machine Learning: Clustering is an Unsupervised Learning technique used to group similar data points together.
• Machine Learning: Clustering is an Unsupervised Learning technique used to group similar data points together based on their features.
• Density-Based Clustering: Density-based clustering algorithms group data points based on their density, identifying dense regions as clusters.
• Unsupervised Learning: Clustering is a type of unsupervised learning where the algorithm groups data points without any labeled training data.
• K-Means: K-Means is a popular clustering algorithm that partitions data into K clusters based on similarity.
• Data Visualization: Clustering results can be visualized using techniques like scatter plots or dendrograms to understand the structure of the data.
• Hierarchical Clustering: Hierarchical clustering builds a hierarchy of clusters by merging or splitting clusters based on their proximity.
• Segmentation: Segmentation often involves clustering techniques to group similar data points together.
• Classification: Clustering is a technique used in classification to group similar things together.
• Machine Learning: Clustering is an unsupervised machine learning technique used to discover patterns and structures in data.
• Anomaly Detection: Clustering can be used for anomaly detection by identifying data points that do not belong to any cluster.
• Grouping: Clustering is a technique used in grouping, where items are grouped based on similarity or proximity.
• Agglomeration: Agglomeration refers to the process of clustering or grouping together of things, people, or activities.
• Data Mining: Clustering is a fundamental technique used in data mining to group similar data points together.
• Segmentation: Clustering is used for segmentation tasks, such as image segmentation or customer segmentation.