Mining closed itemsets in data stream using formal concept analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6263 LNCS, Issue , 2010, Pages 285-296

  Gupta, Anamika ^a   Bhatnagar, Vasudha ^a   Kumar, Naveen ^a  

^a UNIVERSITY OF DELHI   (India)

Author keywords

Closed Itemsets; Data Stream; Landmark Window Model Formal Concept Analysis

Indexed keywords

CANDIDATE GENERATION; CLOSED ITEMSETS; CONCEPT LATTICES; CORE STRUCTURE; DATA STREAM; EXPERIMENTAL EVALUATION; FORMAL CONCEPT ANALYSIS; FREQUENT CLOSED ITEMSETS; LANDMARK WINDOW MODEL FORMAL CONCEPT ANALYSIS; MINING FREQUENT ITEMSETS; OFFLINE; ONLINE COMPONENTS; ONLINE DATA; REAL LIFE DATASETS; SUPPORT THRESHOLD; FORMAL CONCEPT ANALYSES (FCA);

ALGORITHMS; ASSOCIATION RULES; ASSOCIATIVE PROCESSING; DATA COMMUNICATION SYSTEMS; DATA MINING; HYDRAULICS; WAREHOUSES; DATA WAREHOUSES; INFORMATION ANALYSIS;

DATA WAREHOUSES; FORMAL CONCEPT ANALYSIS;

EID: 78049354493     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15105-7_23     Document Type: Conference Paper

References (17)

1. Agarwal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: 20th International Conference on Very Large Databases, pp. 487-499 (1994)
2. Chang, J., Lee, W.: Finding Recent Frequent Itemsets Adaptively over Online Data stream. In: 9th ACM SIGKDD, pp. 487-492. ACM Press, New York (2003)
3. Cheng, J., Ke, Y., Ng, W.: A Survey on Algorithms for Mining Frequent Itemsets over Data stream. KAIS Journal 16(1), 1-27 (2008)
4. Chen, J., Li, S.: GC-Tree: A Fast Online Algorithm for Mining Frequent Closed Itemsets. In: Proceeding of PAKDD Workshop of HPDMA, pp. 457-468 (2007)
5. Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1999)
6. Han, J., Cheng, H., Xin, D., Yan, X.: Frequent Pattern Mining: Current Status and Future Directions. Journal of Data Mining and Knowledge Discovery 15, 55-86 (2007) (Pubitemid 47142811)
7. Jiang, N., Gruenwald, L.: CFI-Stream: Mining Closed Frequent Itemsets in Data stream. In: 12th ACM SIGKDD, Poster Paper, pp. 592-597. ACM Press, New York (2006)
8. Kuznetsov, S.O., Obiedkov, S.A.: Comparing Performance of Algorithms for Generating Concept Lattices. JETAI 14, 189-216 (2002)
9. Li, H., Ho, C., Lee, S.: Incremental Updates of Closed Frequent Itemsets Over Continuous Data stream. Expert Systems with Applications 36, 2451-2458 (2009)
10. Liu, X., Guan, J., Hu, P.: Mining Frequent Closed Itemsets from a landmark window over online data stream. Journal of Computers and Mathematics with Applications 57(6), 927-936 (2009)
11. Pasquier, N., et al.: Efficient Mining of Association Rules using Closed Itemset Lattices. Journal of Information Systems 24(1), 25-46 (1999)
12. Stumme, G., et al.: Computing Iceberg Concept Lattices with Titanic. Journal on Knowledge and Data Engineering 42(2), 189-222 (2002) (Pubitemid 34545019)
13. Valtchev, P., Missaoui, R., Godin, R.: A framework for incremental generation of closed itemsets. Discrete Applied Mathematics 156(6), 924-949 (2008) (Pubitemid 351284490)
14. Chi, Y., Wang, H., Yu, P.S., Muntz, R.R.: Catch the Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window. Journal of Knowledge and Information Systems 10, 265-294 (2006) (Pubitemid 44489997)
15. Yahia, S.B., Hamrouni, T., Nguifo, E.M.: Frequent Closed Itemset Based Algorithms: A thorough structural and analytical survey. ACM SIGKDD Explorations Newsletter 8, 93-104 (2006)
16. Zaki, M.J.: Generating Non-Redundant Association Rules. In: 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 34-43. ACM Press, New York (2000)
17. Zheng, Z., Kohavi, R., Mason, L.: Real World Performance of Association Rule Algorithms. In: Proceedings of the 2001 International Conference Knowledge Discovery and Data Mining, SIGKDD 2001 (2001)