Attractive Neighborhoods: A Comparative Analysis of Tourist Destinations

In this article, we explore a novel algorithm for clustering words in a hierarchical fashion while incorporating both horizontal and vertical structural constraints. The proposed method combines graph coarsening and optimal cuts to efficiently cluster words based on their contextual relationships. By introducing constraints that prioritize linkage between certain words and prevent others from being grouped together, we can enhance the accuracy and relevance of the clustering results.
Our approach is motivated by the need to extract and summarize relevant information in short sentence settings, such as satisfaction questionnaires, hotel reviews, and social media posts. Traditional hierarchical clustering methods often lack the ability to incorporate contextual constraints, which can lead to inaccurate groupings of similar words. By incorporating these constraints, we can improve the efficiency and effectiveness of the clustering process, enabling users to identify patterns and trends that may have otherwise gone unnoticed.
The proposed algorithm represents a significant departure from traditional clustering methods by integrating structural constraints into the clustering process. These constraints are informed by contextual information about the data, such as the relationships between words in a sentence or their position within a hierarchy. By leveraging this contextual information, we can create more accurate and informative clusters that better reflect the underlying structure of the data.
In summary, our proposed algorithm offers a novel approach to constrained hierarchical clustering that enables users to efficiently classify and summarize words based on their contextual relationships. By incorporating both horizontal and vertical structural constraints, we can improve the accuracy and relevance of the clustering results, making it easier to identify patterns and trends in large datasets.