| Summary: | 碩士 === 國立臺灣師範大學 === 圖書資訊學研究所 === 98 === Researchers often use statistics from previous events to serve as a basis for analysis, but the acquired data usually has its problems, which in turn may reduce the efficiency of the researcher’s analysis or even create erroneous results. Libraries often analyze the patron’s borrowing history in order to adjust and improve its services, but often does not consider the patron’s purpose behind borrowing his or her information from the library. Most patrons have several reasons behind their borrowings, and it is may create erroneous results if we don’t clean it before analyzing.
In this paper we analyze the effectiveness of a heuristic data-cleaning approach to remove the areas of non-interest in the patron’s historical loan record. Meanwhile, we also use F-Measure analysis to evaluate the results in order to suggest suitable cleaning methods. In addition, personal cleaning processes for patrons is implemented by adjusting the parameters of the clean-up mechanisms.
From the study results, the patron’s borrowing history cannot be easily cleaned based on interest purposes, but you can attempt to clean the data by the E-M algorithm using cluster analysis, and use the properties of third tier classification: number, loan date, and author. Using personal cleaning, it is concluded that adjustments in the parameters could produce more satisfying results. In addition, if use F-Measure, more interesting parts in the patron’s borrowing history, the cleaning process will be more difficult.
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