Reduced-parameter approaches to time-sensitive discovery and analysis of navigational patterns

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2006
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Abstract
Navigational pattern discovery is the application of pattern mmmg techniques to navigational studies. Previous work on navigational pattern discovery strongly relies on user-specified preset parameters (notably, support thresholds). A major drawback of parameter-driven data mining is setting appropriate thresholds. In most practical scenanos, data mining 1s iterative, meaning that the analyst may mme at varymg thresholds before arriving at satisfactory results. This usually proves expensive m parameter-driven techniques. These challenges faced in parameter-driven data mining become very pronounced in environments with continuously streaming data where it is no longer possible to apply simple iterative data mining techniques. This thesis explores a new design goal for the discovery and analysis of navigational patterns, which aims to reduce the use of pre-defined parameters required at the beginning of the mining process (reduced-parameter mining), and completely removes pre-defined parameters wherever possible (parameter-free mining). The new paradigm is developed in the context of time-sensitive navigational pattern discovery. Three broad dimensions are explored where time can significantly affect the ways navigational patterns are to be discovered or analyzed. The first dimension 1s m information systems where the content continuously changes with time (thus affecting navigational behaviour). The second dimension is in high-volume information systems, where the usage-logs continuously change with time. The final dimension of temporal significance in navigational pattern discovery explored in the thesis is the representation and analysis of navigational patterns as full temporal objects ( or time series). This thesis conceptualizes the notions of time significance in navigational pattern discovery with respect to the dimensions identified above, and then proposes novel techniques for discovering navigational patterns in such environments that are based on reduced-parameter or parameter-free principles. Interestingly, the results from this thesis show that reduced-parameter mining and parameter-free mining are practical concepts. The results also show that reduced-parameter/parameter-free mining techniques out­perform parameter-based mining techniques in environments that require continuous updates of patterns. This is contrary to the general belief that early introduction of pre­defined parameters always improves the mining process. This result has strong implications to other data mining tasks.
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Bibliography: p. 130-142
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Citation
Udechukwu, A. O. (2006). Reduced-parameter approaches to time-sensitive discovery and analysis of navigational patterns (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/437
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