Introduction: Discovering Hidden Patterns in Data Structures
Modern society exists in a flood of data. Extracting meaningful information and discovering patterns within this vast amount of data is a crucial task for businesses and research institutions. In particular, clustering, which forms groups using only the characteristics of the data itself without prior information, has become a core data analysis technique. Hierarchical clustering is one such clustering method, useful for understanding the deep structure of data by hierarchically representing the relationships between data points. This post delves into all aspects of hierarchical clustering, from its basic principles to the latest trends, practical applications, and expert perspectives.
Core Concepts and Principles
Hierarchical clustering proceeds by starting with data as individual clusters and merging similar clusters. This process repeats until all data belongs to a single cluster, and the result is visualized as a tree structure called a dendrogram. The dendrogram allows for an intuitive understanding of the hierarchical relationships and cluster structure between data points.
Merging Methods
Hierarchical clustering is broadly divided into agglomerative (merging) and divisive methods. Agglomerative clustering treats each data point as a single cluster and then merges the most similar clusters in a bottom-up approach. Divisive clustering, on the other hand, treats the entire dataset as a single cluster and then gradually divides the cluster in a top-down approach. Agglomerative methods are generally more commonly used.
Distance Measurement Methods
Various methods exist for measuring the similarity between clusters. The most common method is using Euclidean distance, but various distance measurement methods such as Manhattan distance and cosine similarity can be used depending on the characteristics of the data. Furthermore, depending on the linkage method, single linkage, complete linkage, average linkage, and Ward's linkage are used. Each linkage method differs in how it calculates the distance between clusters, and the appropriate linkage method must be selected according to the structure of the data.
Latest Trends and Changes
Recently, the use of hierarchical clustering in the field of natural language processing has become prominent. As of June 2025, the combination of the BERTopic model and ChatGPT has enabled context-aware natural language data analysis. This represents a shift away from existing word frequency-based analysis, bringing an innovation that structures topics hierarchically by understanding contextual meaning. In addition, the development of a cluster analysis-based rainfall damage prediction function has classified regions with similar damage characteristics through hierarchical cluster analysis and developed rainfall damage prediction models specialized for each cluster. This demonstrates the practical contribution of hierarchical clustering in the field of disaster prediction and management. Furthermore, trend analysis data from 2025 suggests that HRI (Human-Robot Interaction) is a notable trend, and hierarchical clustering is expected to be used in this field for sentiment analysis and user behavior pattern analysis.
Practical Application Plans
Hierarchical clustering contributes to solving practical problems in various fields. In customer segmentation, customers can be grouped based on data such as purchasing behavior, demographics, and preferences, enabling the provision of customized promotions for each group. For example, marketing efficiency can be maximized by providing discount coupons related to specific products to customer groups with a high purchase history for those products, and providing new sign-up benefits to new customer groups. In addition, cluster analysis can be used in environmental impact assessments for eco-friendly dam construction to group regions with similar environmental characteristics and derive optimal construction plans for each group. This will contribute to minimizing environmental destruction and enabling sustainable development. Unlike K-Means, a major advantage of hierarchical clustering is that the number of clusters does not need to be specified in advance.
Expert Recommendations
💡 Technical Insight
Precautions When Introducing Technology: The computational complexity of hierarchical clustering can increase when the data size is large. Therefore, when applying it to large-capacity data, it is important to reduce computational costs by utilizing dimensionality reduction techniques or sampling techniques. In addition, the method of measuring the distance between clusters and the linkage method must be carefully selected. If an appropriate method that matches the characteristics of the data is not selected, incorrect clustering results may be obtained.
Outlook for the Next 3-5 Years: Hierarchical clustering is expected to evolve into a more powerful data analysis tool by converging with artificial intelligence and machine learning technologies. In particular, by combining with deep learning-based representation learning, complex patterns in high-dimensional data can be identified more effectively. In addition, as the demand for explainable artificial intelligence (XAI) increases, the technology to visually explain and interpret the results of hierarchical clustering will become more important.
Conclusion
Hierarchical clustering is a powerful tool for data analysis, driving innovation in various fields. It contributes to solving practical problems in various fields such as natural language processing, disaster prediction, and customer segmentation, and its importance is expected to increase further. In particular, it will enable more advanced forms of data analysis through convergence with artificial intelligence and machine learning technologies. Hierarchical clustering is expected to become a core technology that supports decision-making in future society, going beyond a simple data analysis technique. In a situation where dark pattern regulations are strengthened due to the revision of the Enforcement Decree and Enforcement Rules of the Electronic Commerce Act in 2025, hierarchical clustering will play an even more important role in analyzing and understanding customer data.