Discovering frequent behaviors: Time is an essential element of the context

Knowledge and Information Systems

Volumn 28, Issue 2, 2011, Pages 311-331

  Saleh, Bashar ^a   Masseglia, Florent ^a  

^a INRIA   (France)

Author keywords

Itemsets; Periods; Time aware

Indexed keywords

INFORMATION SYSTEMS;

CHRISTMAS; ESSENTIAL ELEMENTS; ITEM SETS; PERIODS; TIME-AWARE; USAGE MINING;

EXTRACTION;

EID: 79961207270     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-010-0361-5     Document Type: Article

References (34)

1. Agrawal R, Imielinski T, Swami AN (1993) Mining association rules between sets of items in large databases. In: SIGMOD, Washington, pp 207-216.
2. Ale JM, Rossi GH (2000) An approach to discovering temporal association rules. In: SAC'00: Proceedings of the 2000 ACM symposium on applied computing, pp 294-300.
3. Burdick D, Calimlim M, Flannick J, Gehrke J, Yiu T (2005) Mafia: a maximal frequent itemset algorithm. IEEE Trans Knowl Data Eng 17(11): 1490-1504.
4. Calders T, Dexters N, Goethals B (2007) Mining frequent itemsets in a stream. In: ICDM, pp 83-92.
5. Chang JH, Lee WS (2003) Finding recent frequent itemsets adaptively over online data streams. In: KDD'03: Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining, pp 487-492.
6. Chen X, Petrounias I (1999) Mining temporal features in association rules. In: PKDD'99: Proceedings of the 3rd European conference on principles of data mining and knowledge discovery, pp 295-300.
7. Fung C, Xu J, Yu PSY, Lu H (2005) Parameter free bursty events detection in text streams. In: VLDB'05: Proceedings of the 31st international conference on very large data bases, pp 181-192.
8. Chi Y, Wang H, Yu PS, Muntz RR (2006) Catch the moment: maintaining closed frequent itemsets over a data stream sliding window. Knowl Inf Syst 10(3): 265-294.
9. Chong Z, Yu JX, Lu H, Zhang Z, Zhou A (2005) False-negative frequent items mining from data streams with bursting. In: DASFAA'05: Database systems for advanced applications, pp 422-434.
10. Crepeau RC (2010) The economics of super bowl xliii. [Online], http://www. poppolitics. com/archives/2009/01/the-economics-of-super-bowl-xliii.
11. Duncan A (2010) Super bowl xxxv fun facts. [Online], http://advertising. about. com/od/superbowlcoverage/a/xxxvfunfacts. htm.
12. Gao C, Wang J (2009) Efficient itemset generator discovery over a stream sliding window. In: CIKM'09: Proceeding of the 18th ACM conference on information and knowledge management. ACM, New York, pp 355-364.
13. Giannella C, Han J, Pei J, Yan X, Yu P (2003) Mining frequent patterns in data streams at multiple time granularities. In: Kargupta H, Joshi A, Sivakumar K, Yesha Y (eds) Next generation data mining. AAAI/MIT.
14. Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: SIGMOD, pp 1-12.
15. Bayardo RJ Jr (1998) Efficiently mining long patterns from databases. In: SIGMOD, June 2-4, Seattle, pp 85-93.
16. Lee C-H, Lin C-R, Chen M-S (2001) On mining general temporal association rules in a publication database. In: ICDM 29 Nov-2 Dec 2001, San Jose, pp 337-344.
17. Li Y, Ning P, Wang XS, Jajodia S (2003) Discovering calendar-based temporal association rules. Data Knowl Eng 44(2): 193-218.
18. Lian W, Cheung DW, Yiu SM (2007) Maintenance of maximal frequent itemsets in large databases. In SAC, pp 388-392.
19. Lucchese C, Orlando S, Perego R (2006) Fast and memory efficient mining of frequent closed itemsets. IEEE Trans Knowl Data Eng 18(1): 21-36.
20. Masseglia F, Poncelet P, Teisseire M, Marascu A (2008) Web usage mining: extracting unexpected periods from web logs. Data Min Knowl Discov 16(1): 39-65.
21. Vlachos M, Wu K-L, Chen S-K, Yu PS (2005) Fast burst correlation of financial data. In: Knowledge discovery in databases: PKDD 2005, pp 422-434.
22. Ozden B, Ramaswamy S, Silberschatz A (1998) Cyclic association rules. In: ICDE, Orlando, pp 412-421.
23. Palma AT, Bogorny V, Kuijpers B, Alvares LO (2008) A clustering-based approach for discovering interesting places in trajectories. In: SAC, pp 863-868.
24. Palshikar GK, Kale MS, Apte MM (2007) Association rules mining using heavy itemsets. Data Knowl Eng 61(1): 93-113.
25. Pasquier N, Bastide Y, Taouil R, Lakhal L (1999) Discovering frequent closed itemsets for association rules. In: ICDT, pp 398-416.
26. Roddick JF, Spiliopoulou M (2002) A survey of temporal knowledge discovery paradigms and methods. IEEE TKDE 14(4): 750-767.
27. Saleh B, Masseglia F (2008) Time aware mining of itemsets. In: TIME, pp 93-97.
28. Teng W-G, Chen M-S, Yu PS (2003) A regression-based temporal pattern mining scheme for data streams. In: VLDB, pp 93-104.
29. Toivonen H (1996) Sampling large databases for association rules. In: VLDB, 3-6 Sep, Mumbai (Bombay), pp 134-145.
30. Wang J, Han J, Pei J (2003) Closet+: searching for the best strategies for mining frequent closed itemsets. In: KDD, Washington, 24-27 Aug, pp 236-245.
31. Xiong H, Steinbach M, Ruslim A, Kumar V (2009) Characterizing pattern preserving clustering. Knowl Inf Syst 19(3): 311-336.
32. Yoo JS, Zhang P, Shekhar S (2005) Mining time-profiled associations: an extended abstract. In: PAKDD, Hanoi, 18-20 May, pp 136-142.
33. Zhang S, Wu X, Zhang C, Lu J (2008) Computing the minimum-support for mining frequent patterns. Knowl Inf Syst 15(2): 233-257.
34. Zhu Y, Shasha D (2003) Efficient elastic burst detection in data streams. In: KDD'03: Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining, pp 336-345.