Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Topic Discovery from Text Using Aggregation of Different Clustering Methods
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Information Retrieval: Algorithms and Heuristics (The Kluwer International Series on Information Retrieval)
Hierarchical Clustering Algorithms for Document Datasets
Data Mining and Knowledge Discovery
Topic discovery based on text mining techniques
Information Processing and Management: an International Journal
A survey of Web clustering engines
ACM Computing Surveys (CSUR)
Dynamicity vs. effectiveness: studying online clustering for scatter/gather
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
The cluster-abstraction model: unsupervised learning of topic hierarchies from text data
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Analysis of structural relationships for hierarchical cluster labeling
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes
The Journal of Machine Learning Research
Creating topic hierarchies for large medical libraries
KR4HC'09 Proceedings of the 2009 AIME international conference on Knowledge Representation for Health-Care: data, Processes and Guidelines
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Incremental learning of topic hierarchies is very useful to organize and manage growing text collections, thereby summarizing the implicit knowledge from textual data. However, currently available methods have some limitations to perform the incremental learning phase. In particular, when the initial topic hierarchy is not suitable for modeling the data, new documents are inserted into inappropriate topics and this error gets propagated into future hierarchy updates, thus decreasing the quality of the knowledge extraction process. We introduce a method for obtaining more robust initial topic hierarchies by using consensus clustering. Experimental results on several text collections show that our method significantly reduces the degradation of the topic hierarchies during the incremental learning compared to a traditional method.