KEA: practical automatic keyphrase extraction
Proceedings of the fourth ACM conference on Digital libraries
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
The Growing Hierarchical Self-Organizing Map
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
KPSpotter: a flexible information gain-based keyphrase extraction system
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
Mining Imperfect Data: Dealing with Contamination and Incomplete Records
Mining Imperfect Data: Dealing with Contamination and Incomplete Records
Generative model-based document clustering: a comparative study
Knowledge and Information Systems
Semantic Smoothing for Model-based Document Clustering
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
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The general goal of clustering is to group data elements such that the intra-group similarities are high and the inter-group similarities are low. In this paper, we propose a novel hybrid clustering technique that incorporates semantic smoothing of document models into a neural network framework. Recently it has been reported that the semantic smoothing model enhances the retrieval quality in Information Retrieval (IR). Inspired by that, we apply the context-sensitive semantic smoothing model to boost accuracy of clustering that is generated by a dynamic growing cell structure algorithm, a variation of the neural network technique. We evaluated the proposed technique on article sets from MEDLINE, the largest biomedical digital library in Biomedicine. Our experimental evaluations show that the proposed algorithm significantly improves the clustering quality over the traditional clustering techniques.