Clustering Algorithms - Hierarchical and Density-based: Analyzing hierarchical and density-based clustering algorithms for grouping similar data points together in unlabeled datasets
Keywords:
Data mining, Hierarchical clusteringAbstract
Clustering algorithms play a crucial role in unsupervised learning, enabling the grouping of similar data points into clusters. Hierarchical clustering and density-based clustering are two widely used approaches for this purpose. This paper provides a comprehensive analysis of these clustering algorithms, focusing on their principles, methodologies, strengths, and weaknesses. We discuss how hierarchical clustering builds a tree of clusters, allowing for a hierarchical representation of the data, while density-based clustering identifies regions of high density as clusters.
The paper explores the applications of these algorithms in various fields, including data mining, pattern recognition, and image analysis. We also discuss the challenges associated with these algorithms, such as scalability and parameter sensitivity, and propose potential solutions. Through experimental evaluations on benchmark datasets, we compare the performance of hierarchical and density-based clustering algorithms in terms of clustering quality, scalability, and robustness to noise.
Overall, this paper aims to provide a comprehensive understanding of hierarchical and density-based clustering algorithms, their applications, and their comparative analysis, offering insights into their effectiveness and limitations in real-world scenarios.
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