44 : 1 (Fall 2006) : 35-60 35 E-mail: jcshieh@ntnu.edu.tw E-mail: zaire@ms49.hinet.net bibliomining library automation system library integrated system 2006/10/11 ; 2006/11/18 ; 2006/12/05
36 44 : 1 (Fall 2006) Nicholson 2003 bibliomining-data mining for libraries data mining bibliometric data warehousing Han & Kamber, 2006 1 ㈠ data cleaning and integration
37 data pre-processing ㈡ data selection and transformation ㈢ data mining / ㈣ evaluation and presentation 1
38 44 : 1 (Fall 2006) customer relationship management 2004 Jiao & Zhang, 2004 Chiang, Wang, Lee & Lin, 2004 2003 2003 2003 2003 ㈠ Nicholson 2003 data mining in library Schulman 1998 Guenther 2000 Nicholson 2003 Papatheodorou 2003
39 1. Larsen 1996 Mancini 1996 Atkins 1996 Peters 1996 2. Banerjee 1998 Peters 1996
40 44 : 1 (Fall 2006) 3. Lawrence 1999 Ronald 2001 citation ㈡ Nicholson 2003 1. determining areas of focus patterns 2. identifying internal and external data sources 3. collecting, cleaning and anonymzing the data into a data warehouse SQL 4. selecting appropriate analysis tools on-line analytical processing 5. discovery of patterns through data mining
41 description prediction 6. analyzing and implementing the results ㈢ 1. 2002 2002 2002 2002 2001
42 44 : 1 (Fall 2006) 2001 large item sets interesting rules 2001 2001 H-Mine H-Mine Kao, Hang & Lin 2003 Wu 2003 data mining based model DMBA 2002 000~999 2002 2. Neumann 2003 amazon.com 3.
43 2002 2001 4. 2002 2001 Neumann 2003 2002 1 1
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47 ㈣ 1. SPSS Clementine 8.1 KDnuggets2003 5 www.kdnuggets.com SPSS Clementine Clementine 8.1 flat file ODBC association analysis sequence pattern analysis regression analysis cluster analysis decision tree analysis neural network analysis data reduction analysis 2. TOTALS Technology Opulent TRANSTECH Automated Library System 2 T2 T2 Unix NT T2 WebPAC 3. Intel Pentium 4 1.6MHz CPU 512MB 120GB Microsoft Windows NT 4.0 Microsoft SQL Server 7.0 ㈤ 1. 2.
48 44 : 1 (Fall 2006) 3. 80% 92 8 93 7 snowflake schema 3
49 1. 2 2 12 10 3 7 8 6 12 16 9 13 12 10 3 4 5 6 9 6 7 8 12 12 16 9 13 3
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51 2. a. 12 12 12 10 9 b. 4 4 6 10 c. 857 855 874 861 873 876 d. 16 15 8 9
52 44 : 1 (Fall 2006) 16 e. 857 855 873 874 861 857 874 855 857 857 861 861 873 874 a. b. 857 5 6 5 330 308 308 330
53 6 857 874 876 873 857 312 330 312 308 857 c. 7 857 874 861 855 330 340 310 340 330 308 997 995 330 310 340 312
54 44 : 1 (Fall 2006) 2 3 855 857 56 42 68 54 857 874 861 56 42 68 54 857 874 876 873 12 15 16 12 ㈠ 12 12 16
55 ㈡ 857 855 874 873 874 873 ㈢ 330 340 310
56 44 : 1 (Fall 2006) 857 855 874 873 ㈠ ㈡ ETL Extraction Transfer Loading
57 ㈢ 2001 66 59-72 2002 17 81-94 2002 2002 2001 2003 2003 2002 91 1 161-195 2001 2002 2001 Web Mining 2005 50 76-91 2002 2003 2004
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Journal of Educational Media & Library Sciences 44 : 1 (Fall 2006) : 35-60 Bibliomining User Behaviors in the Library Jiann-Cherng Shieh Associate Professor Graduate Institute of Library & Information Studies National Taiwan Normal University Taipei, R.O.C E-mail: jcshieh@ntnu.edu.tw Yung-Shun Lin Abstract The Affiliated Senior High School of NTNU Taipei, R.O.C E-mail: zaire@ms49.hinet.net The information discovered through data analysis and data mining can be great helpful for decision makings in organizations. For servicing users complacence, libraries should actively explore the user needs and then provide them with required information. It is the critical task for libraries in this age. Bibliomining, data mining applied to library operations, can really assist in gasping patrons requirements. Based on circulation data, the previous works have provided many suggestions about library management. In this research, we try to comprehend more data, not only circulation data but also various patrons-related data, to bibliomining their behavior in libraries. The results can be used as crucial and clinical information to aid libraries in collection policy making, material recommendation, budget allocation and other library management related issues. Keywords: Bibliomining; Data mining; User behavior