User Oriented Hierarchical Information Organization and Retrieval
ECML '07 Proceedings of the 18th European conference on Machine Learning
A survey of Web clustering engines
ACM Computing Surveys (CSUR)
Comparison of similarity measures for clustering Turkish documents
Intelligent Data Analysis
Clustering with relative constraints
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
| Hi-index | 0.02 |
A hierarchical structure can provide efficient access to information contained in a collection of documents. However, such a structure is not always available, e.g. for a set of documents a user has collected over time in a single folder or the results of a web search. We therefore investigate in this paper how we can obtain a hierarchical structure automatically, taking into account some background knowledge about the way a specific user would structure the collection. More specifically, we adapt a hierarchical agglomerative clustering algorithm to take into account user specific constraints on the clustering process. Such an algorithm could be applied, e.g., for user specific clustering of Web search results, where the user's constraints on the clustering process are given by a hierarchical folder or bookmark structure. Besides the discussion of the algorithm itself, we motivate application scenarios and present an evaluation of the proposed algorithm on benchmark data.