An Aging Theory for Event Life-Cycle Modeling

An event can be described by a sequence of chronological documents from several information sources that together describe a story or happening. The goal of event detection and tracking is to automatically identify events and their associated documents during their life cycles. Conventional document...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part A, Systems and humans man and cybernetics. Part A, Systems and humans, 2007-03, Vol.37 (2), p.237-248
Main Authors: Chen, Chien Chin, Chen, Yao-Tsung, Chen, Meng Chang
Format: Article
Language:English
Subjects:
Aging
Algorithms
Classification
Clustering
Clustering algorithms
Cybernetics
Event detection
Information science
Internet
knowledge life cycle
Life cycle engineering
Mathematical models
Publishing
Search engines
Studies
Temporal logic
Text categorization
Tracking
Web mining
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Summary:An event can be described by a sequence of chronological documents from several information sources that together describe a story or happening. The goal of event detection and tracking is to automatically identify events and their associated documents during their life cycles. Conventional document clustering and classification techniques cannot effectively detect and track sequential events, as they ignore the temporal relationships among documents related to an event. The life cycle of an event is analogous to living beings. With abundant nourishment (i.e., related documents for the event), the life cycle is prolonged; conversely, an event or living fades away when nourishment is exhausted. Improper tracking algorithms often unnecessarily prolong or shorten the life cycle of detected events. In this paper, we propose an aging theory to model the life cycle of sequential events, which incorporates a traditional single-pass clustering algorithm to detect and track events. Our experiment results show that the proposed method achieves a better overall performance for both long-running and short-term events than previous approaches. Moreover, we find that the aging parameters of the aging schemes are profile dependent and that using proper profile-specific aging parameters improves the detection and tracking performance further
ISSN:1083-4427
2168-2216
1558-2426
2168-2232
DOI:10.1109/TSMCA.2006.886370