Using background knowledge to rank itemsets

Data Mining and Knowledge Discovery

Volumn 21, Issue 2, 2010, Pages 293-309

  Tatti, Nikolaj ^a   Mampaey, Michael ^a  

^a UNIVERSITY OF ANTWERP   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ADDITIONAL KNOWLEDGE; BACKGROUND KNOWLEDGE; FREQUENCY PREDICTION; INDEPENDENCE MODELS; ITEM SETS; MAXIMUM ENTROPY; MAXIMUM ENTROPY MODELS; OPEN PROBLEMS; POLYNOMIAL-TIME;

DATA MINING; ENTROPY; POLYNOMIAL APPROXIMATION;

INFORMATION USE;

EID: 77958060227     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-010-0188-4     Document Type: Conference Paper

References (21)

1. Aggarwal CC, Yu PS (1998) A new framework for itemset generation. In: PODS '98: Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems. ACM Press, New York, pp 18-24
2. Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo AI (1996) Fast discovery of association rules. In: Advances in knowledge discovery and data mining. AAAI/MIT Press, Boston, pp 307-328
3. Brijs T, Swinnen G, Vanhoof K, Wets G (1999) Using association rules for product assortment decisions: a case study. In: Knowledge discovery and data mining, pp 254-260
4. Brin S, Motwani R, Silverstein C (1997) Beyond market baskets: generalizing association rules to correlations. In: Peckham J (ed) SIGMOD 1997, Proceedings ACM SIGMOD international conference on management of data, May. ACM Press, New York, pp 265-276
5. Cowell RG, Dawid AP, Lauritzen SL, Spiegelhalter DJ (1999) Probabilistic networks and expert systems. Statistics for engineering and information science. Springer, New York
6. Csiszár I (1975) I-divergence geometry of probability distributions andminimization problems. Ann Probab 3(1):146-158
7. Darroch J, RatcliffD (1972) Generalized iterative scaling for log-linear models.Ann Math Stat 43(5):1470-1480
8. Garriga GC, Junttila E, Mannila H (2008) Banded structure in binary matrices. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, Las Vegas, Nevada, USA, August 24-27, 2008. ACM, New York, pp 292-300
9. Gionis A, Mannila H, Mielikäinen T, Tsaparas P (2007) Assessing data mining results via swap randomization. TKDD 1(3)
10. Hanhijärvi S, Ojala M, Vuokko N, Puolamäki K, Tatti N, Mannila H (2009) Tell me something I don't know: randomization strategies for iterative data mining. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining (KDD 2009), pp 379-388
11. Jaroszewicz S, Scheffer T (2005) Fast discovery of unexpected patterns in data, relative to a Bayesian network. In: KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. ACM, New York, pp 118-127
12. Jaroszewicz S, Simovici DA (2004) Interestingness of frequent itemsets using Bayesian networks as background knowledge. In: KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, pp 178-186
13. Kohavi R, Brodley C, Frasca B, Mason L, Zheng Z (2000) KDD-Cup 2000 organizers' report: peeling the onion. SIGKDD Explor 2(2):86-98. http://www.ecn.purdue.edu/KDDCUP
14. Mannila H, Terzi E (2007) Nestedness and segmented nestedness. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, p 489
15. Meo R (2000) Theory of dependence values. ACM Trans Database Syst 25(3):380-406
16. Myllykangas S, Himberg J, Böhling T, Nagy B, Hollmén J, Knuutila S (2006) DNA copy number amplification profiling of human neoplasms. Oncogene 25(55):7324-7332
17. Rasch G (1960) Probabilistic models for some intelligence and attainment tests. Danmarks paedagogiske Institut, Copenhagen
18. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461-464
19. Tatti N (2006) Safe projections of binary data sets. Acta Inf 42(8-9):617-638
20. Tatti N (2008) Maximum entropy based significance of itemsets. Knowl Inf Syst 17(1):57-77
21. Zaki JZ (2000) Scalable algorithms for association mining. IEEE Trans Knowl Data Eng 12(3):372-390