Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Lexical analysis and stoplists
Information retrieval
Probabilistic models in cluster analysis
Computational Statistics & Data Analysis - Special issue on classification
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
Proceedings of the ninth international conference on Information and knowledge management
Concept decompositions for large sparse text data using clustering
Machine Learning
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive dimension reduction for clustering high dimensional data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Ontologies Improve Text Document Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Flexible intrinsic evaluation of hierarchical clustering for TDT
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Editorial: Defining Interesting Research Problems
Marketing Science
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
Data mining from 1994 to 2004: an application-orientated review
International Journal of Business Intelligence and Data Mining
The heavy frequency vector-based text clustering
International Journal of Business Intelligence and Data Mining
A clustering algorithm based on an estimated distribution model
International Journal of Business Intelligence and Data Mining
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The rapid increase of publications in marketing and related areas increasingly hampers the realisation of a general idea of what is 'hot' in the respective fields of interest. Topic Detection (TD), based on unsupervised text clustering, is a promising approach to tackle this problem. We introduce a new methodology that facilitates the determination of the number of topics discussed in a given text collection. By applying this approach to a text corpus which includes 12 international marketing and business journals we identify hot spots in marketing science. The approach may help both scientists and practitioners to systematically discover topics in digital information environments, as provided by the internet for instance.