A framework for measuring changes in data characteristics
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Introduction to topic detection and tracking
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Tracking dynamics of topic trends using a finite mixture model
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On Change Diagnosis in Evolving Data Streams
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Discovering evolutionary theme patterns from text: an exploration of temporal text mining
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Detection of emerging space-time clusters
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Expanding the taxonomies of bibliographic archives with persistent long-term themes
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MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
MONIC: modeling and monitoring cluster transitions
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Anticipating annotations and emerging trends in biomedical literature
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STORIES in Time: A Graph-Based Interface for News Tracking and Discovery
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Detecting topic evolution in scientific literature: how can citations help?
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Domain-specific identification of topics and trends in the blogosphere
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Following the social media: aspect evolution of online discussion
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Comparing clustering algorithms and their influence on the evolution of labeled clusters
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Story graphs: Tracking document set evolution using dynamic graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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As document collections accummulate over time, some of the discussion subjects in them become outfashioned, while new ones emerge. Then, old classification schemes should be updated. In this paper, we address the challenge of finding emerging and persistent “themes”, i.e. subjects that live long enough to be incorporated into a taxonomy or ontology describing the document collection. We focus on the identification of cluster labels that “survive” changes in the constitution of the underlying population of documents, including changes in the feature space of dominant words, because the terminology of the document archive also changes over time. We have conducted a set of promising experiments on the identification of themes that manifested themselves in section H2.8 of the ACM digital library and juxtapose them with the classes foreseen in the ACM taxonomy for this section.