GTM: the generative topographic mapping
Neural Computation
Mapping a manifold of perceptual observations
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
Neural Computation
Websom for Textual Data Mining
Artificial Intelligence Review - Special issue on data mining on the Internet
Proceedings of the 1998 conference on Advances in neural information processing systems II
A Combined Latent Class and Trait Model for the Analysis and Visualization of Discrete Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Using machine learning to improve information access
Using machine learning to improve information access
Complexity Pursuit: Separating Interesting Components from Time Series
Neural Computation
Topic Identification in Dynamical Text by Complexity Pursuit
Neural Processing Letters
A generative probabilistic approach to visualizing sets of symbolic sequences
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thread detection in dynamic text message streams
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Visualization of text streams: a survey
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Visualization of temporal text collections based on Correspondence Analysis
Expert Systems with Applications: An International Journal
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We propose a novel probabilistic method, based on latent variable models, for unsupervised topographic visualisation of dynamically evolving, coherent textual information. This can be seen as a complementary tool for topic detection and tracking applications. This is achieved by the exploitation of the a priori domain knowledge available, that there are relatively homogeneous temporal segments in the data stream. In a different manner from topographical techniques previously utilized for static text collections, the topography is an outcome of the coherence in time of the data stream in the proposed model. Simulation results on both toy-data settings and an actual application on Internet chat line discussion analysis is presented by way of demonstration.