Towards personalized recommendation by two-step modified Apriori data mining algorithm

Expert Systems with Applications

Volumn 35, Issue 3, 2008, Pages 1422-1429

  Lazcorreta, Enrique ^a   Botella, Federico ^a   Fernández Caballero, Antonio ^b,c  

^a MIGUEL HERNÁNDEZ UNIVERSITY   (Spain)

^b INSTITUTO DE INVESTIGACIÓN EN INFORMÁTICA DE ALBACETE   (Spain)

^c UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

Author keywords

Apriori like algorithm; Data mining; Personalization; Recommendation

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; ALGORITHMS; ASSOCIATION RULES; ASSOCIATIVE PROCESSING; BOOLEAN FUNCTIONS; COMPUTER NETWORKS; COMPUTER SYSTEMS; DATA MINING; DECISION SUPPORT SYSTEMS; DIFFRACTIVE OPTICAL ELEMENTS; E-LEARNING; EDUCATION COMPUTING; ELECTRONIC COMMERCE; EVOLUTIONARY ALGORITHMS; HELIUM; INFORMATION MANAGEMENT; INFORMATION SYSTEMS; INTERNET; KNOWLEDGE MANAGEMENT; LEARNING ALGORITHMS; MANAGEMENT INFORMATION SYSTEMS; MINING; MULTIMEDIA SYSTEMS; OIL WELL LOGGING; PAPER; SCHEDULING ALGORITHMS; SEARCH ENGINES; SITE SELECTION; WEBSITES; WORLD WIDE WEB;

(E ,3E) PROCESS; A-PRIORI; APRIORI ALGORITHMS; DATA MINING ALGORITHMS; E LEARNING; E-COMMERCE; EFFICIENT IMPLEMENTATION; ELSEVIER (CO); IN ORDER; LOG FILES; MEDIUM SIZE; NEW METHODS; PERSONALIZED RECOMMENDATION; SINGLE-USER (SU); WEB SITE; WEB SYSTEMS; WEB-BASED INFORMATION SYSTEMS;

MATHEMATICAL MODELS;

EID: 44949263923     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.08.048     Document Type: Article

References (22)

1. Agrawal, R., Imielinski, T., & Swami, A. (1993) Mining association between sets of items in massive database. In: International proceedings of the ACM-SIGMOD international conference on management of data (pp. 207-216).
2. Agrawal, R., & Srikant, R. (1994) Fast algorithms for mining association rules. In: Proceedings of the international conference on very large data bases (pp. 407-419).
3. Cho Y.H., Kim J.K., and Kim S.H. A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications 23 (2002) 329-342
4. Dong J., and Han M. BitTableFI: An efficient mining frequent itemsets algorithm. Knowledge-Based Systems 20 (2007) 329-335
5. Han J., and Kamber M. Data mining: Concepts and techniques (2001), Morgan Kaufmann Publisher
6. Hong T.P., Kuo C.S., and Wang S.L. A fuzzy AprioriTid mining algorithm with reduced computational time. Applied Soft Computing 5 (2004) 1-10
7. Hu Y.H., and Chen Y.L. Mining association rules with multiple minimum supports: A new mining algorithm and a support tuning mechanism. Decision Support Systems 42 (2006) 1-24
8. Kim K.J., and Cho S.B. Personalized mining of web documents using link structures and fuzzy concept networks. Applied Soft Computing 7 (2007) 398-410
9. Kouris I.N., Makris C.H., and Tsakalidis A.K. Using Information Retrieval techniques for supporting data mining. Data & Knowledge Engineering 52 (2005) 353-383
10. Lee Y.C., Hong T.P., and Lin W.Y. Mining association rules with multiple minimum supports using maximum constraints. International Journal of Approximate Reasoning 40 (2005) 44-54
11. Lin D., and Kedem Z.M. Pincer-search: An efficient algorithm for discovering the maximum frequent set. IEEE Transactions on Knowledge Data Engineering 14 (2002) 553-556
12. Liu, B., Hsu, W., & Ma, Y. (1999) Mining association rules with multiple minimum supports. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining (pp. 337-341).
13. Ozmutlu H.C., Spink A., and Ozmutlu S. Analysis of large data logs: An application of Poisson sampling on excite web queries. Information Processing and Management 38 (2002) 473-490
14. Palshikar G.K., Kale M.S., and Apte M.M. Association rules mining using heavy itemsets. Data & Knowledge Engineering 61 (2007) 93-113
15. Perkowitz M., and Etzioni O. Towards adaptive Web sites: Conceptual framework and case study. Artificial Intelligence 118 (2000) 245-275
16. Pujari A.K. Data mining techniques (2001), University Press
17. Saglam B., Salman F.S., Sayin S., and Türkay M. A mixed-integer programming approach to the clustering problem with an application in customer segmentation. European Journal of Operational Research 173 (2006) 866-879
18. Schafer J., Konstan J., and Riedl J. E-commerce recommendation applications. Data Mining and Knowledge Discovery 5 (2001) 115-153
19. Thabtah F., Cowling P., and Hammoud S. Improving rule sorting, predictive accuracy and training time in associative classification. Expert Systems with Applications 31 (2006) 414-426
20. Tsay Y.J., and Chiang J.Y. CBAR: An efficient method for mining association rules. Knowledge-Based Systems 18 (2005) 99-105
21. Tseng M.C., and Lin W.Y. Efficient mining of generalized association rules with non-uniform minimum support. Data & Knowledge Engineering 62 (2007) 41-64
22. Wang Y., Lim E.P., and Hwang S.Y. Efficient mining of group patterns from user movement data. Data & Knowledge Engineering 57 (2006) 240-282