Abstract
Extraction of sequences of events from news and other documents based on the publication times of these documents has been shown to be extremely effective in tracking past events. This paper addresses the issue of constructing an optimal information preserving decomposition of the time period associated with a given document set, i.e., a decomposition with the smallest number of subintervals, subject to no loss of information. We introduce the notion of the compressed interval decomposition, where each subinterval consists of consecutive time points having identical information content. We define optimality, and show that any optimal information preserving decomposition of the time period is a refinement of the compressed interval decomposition. We define several special classes of measure functions (functions that measure the prevalence of keywords in the document set and assign them numeric values), based on their effect on the information computed as document sets are combined. We give algorithms, appropriate for different classes of measure functions, for computing an optimal information preserving decomposition of a given document set. We studied the effectiveness of these algorithms by computing several compressed interval and information preserving decompositions for a subset of the Reuters–21578 document set. The experiments support the obvious conclusion that the temporal information gleaned from a document set is strongly dependent on the measure function used and on other user-defined parameters.
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Available at http://kdd.ics.uci.edu.
Each \( \Pi_C^{w_p} \) is a partition of T D into subintervals. The product of a set of partitions is the coarsest partition that is a refinement of each of the individual partitions, and is unique (Rosen, 2003).
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Acknowledgments
We thank the anonymous referees for their detailed and helpful comments which greatly improved the readability of the paper.
This publication was made possible by NIH grant number P20 RR16469 from the INBRE program of the National Center for Research Resources. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIH.
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A preliminary version of this paper was presented at the 2004 SIAM International Conference on Data Mining.
†Research supported by NSF Grant CCR–0105536.
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Chundi, P., Rosenkrantz, D.J. Information Preserving Time Decompositions of Time Stamped Documents* . Data Min Knowl Disc 13, 41–65 (2006). https://doi.org/10.1007/s10618-005-0035-1
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DOI: https://doi.org/10.1007/s10618-005-0035-1