Hierarchical Clustering with Structured Data

Pros:

• Does not require the number of clusters to be specified in advance.
• Provides a hierarchical structure of clusters, allowing for better understanding of the data.
• Can handle different types of distance measures and linkage methods.

Cons:

• Computationally expensive for large datasets.
• Sensitive to outliers, noise, and the order of data points.
• Difficult to determine the appropriate cut-off threshold for clustering.

1. Customer Segmentation: Analyze customer data to identify distinct groups based on their behavior, preferences, or demographics.
2. Fraud Detection: Cluster financial transactions to detect anomalies or suspicious patterns indicative of fraudulent activities.
3. Image Segmentation: Cluster pixels in images with structured data to segment different objects or regions.

1. scikit-learn Documentation: This page provides detailed documentation on hierarchical clustering with structured data using scikit-learn, a popular machine learning library.
2. Towards Data Science Article: This article explains the concepts and implementation of hierarchical clustering, including the use of structured data.
3. Analytics Vidhya Tutorial: This tutorial provides a step-by-step guide to hierarchical clustering and gives insights into interpreting the results.

1. Peter J. Rousseeuw: Peter is a highly cited researcher in the field of clustering and has published several influential papers on the topic.
2. Daniel Muellner: Daniel is a developer of the fastcluster library, which provides efficient hierarchical clustering algorithms.
3. Haeun Moon: Haeun has expertise in applying hierarchical clustering to diverse domains, such as bioinformatics and data visualization.
4. Linfeng Liu: Linfeng has contributed to various clustering algorithms, including hierarchical clustering, and has experience in handling structured data.
5. Derek Greene: Derek has published research on clustering and has experience in applying it to real-world datasets.

Note: The GitHub profiles provided for the experts may not exclusively focus on hierarchical clustering, but they have significant expertise in the field of clustering in general.