Analysis of Sequential Book Loan Data Pattern Using Generalized Sequential Pattern (GSP) Algorithm

DOI https://doi.org/10.47738/ijiis.v2i1.10
Tri Astuti
Amikom University Purwokerto, Indonesia
Lisdya Anggraini
Amikom University Purwokerto, Indonesia
As a center for learning and information services, STMIK Amikom Purwokerto Library is a source of learning and a source of intellectual activity that is very important for the entire academic community in supporting the achievement of the college Tridharma program. Book lending transaction data, can produce information that is important as supporting decision making when further analyzed. One useful information is that it can provide information in the form of user behavior patterns in borrowing books that are used to maintain the availability of related book stocks to be balanced. This study uses the Generalized Sequential Pattern (GSP) algorithm, which can be used to determine the behavior patterns of users in each transaction and can show relationships or associations between books, both requested simultaneously and sequentially. From the calculations that have been done, 295 frequent sequences are consisting of 3 sequence patterns that are formed from the minimum support of 0.53% or the minimum number of books borrowed, namely 2 books. Three book items have very strong linkages in book lending transactions, namely book code 6690, 2026, and 8131.
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Astuti, T., & Anggraini, L. (2019). Analysis of Sequential Book Loan Data Pattern Using Generalized Sequential Pattern (GSP) Algorithm. International Journal of Informatics and Information Systems, 2(1), 17–23. https://doi.org/10.47738/ijiis.v2i1.10
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  1. P. N. Tan, M. Stenbach, and V. Kumar, Introduction to Data Mining. Boston: Pearson Education, 2006.
  2. F. Goronescu, Data Mining: Concepts, Models, and Techniques. Verlag Berlin Heidelberg: Springer. 2011.
  3. I. H. Witten, E. Frank, and M. A. Hall. Data Mining: Practical Mchine Learning Tools and Techniques, 3rd ed. Burlington, MA: Morgan Kaufmann, 2011.
  4. Berkhin, P., 2002. A Survey of Clustering Data Mining Techniques, Technical Report. Accrue Software, San Jose, CA.
  5. Chen, C.-H., Hong, T.-P., Tseng, V.S., Lee, C.-S., 2009. A genetic-fuzzy mining approach for items with multiple minimum supports. Soft Computing A Fusion of Foundations, Methodologies and Applications 13 (5), 521533.
  6. Chen, S.-S., Huang, T.C.-K., Lin, Z.-M., 2011. New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports. Journal of Systems and Software 84 (10), 16381651.
  7. Chiang, D.-A., Wang, Y.-H., Chen, S.-P., 2010. Analysis on repeat-buying patterns. Knowledge-Based Systems 23 (8), 757768.
  8. Chu, C.-J., Tseng, V.S., Liang, T., 2008. An efficient algorithm for mining temporal high utility itemsets from data streams. Journal of Systems and Software 81 (7), 11051117.
  9. Chun, S., Park, Y., 2006. A new hybrid data mining technique using a regression case based reasoning: application to financial forecasting. Expert Systems with Applications 31, 329336.
  10. Ezeife, C., Lu, Y., 2005. Mining web log sequential patterns with position coded preorder linked wap-tree. Data Mining and Knowledge Discovery 10, 538.
  11. Han, J., Pei, J., Yin, Y., Mao, R., 2004. Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Knowledge Discovery 8, 5387.
  12. Han, J., Cheng, H., Xin, D., Yan, X., 2007. Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery 15, 5586.
  13. Hu, Y.-H., Chen, Y.-L., 2006. Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism. Decision Support Systems 42, 124.
  14. Hu, Y.-H., Wu, F., Liao, Y.-C., 2010. Sequential pattern mining with multiple minimum supports: a tree based approach. In: The 2nd International Conference on Software Engineering and Data Mining, Chengdu, China.
  15. Kim, E., Kim, W., Lee, Y., 2003. Combination of multiple classifiers for the customers purchase behavior prediction. Decision Support Systems 34, 167175.

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