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Wilhelmiina Hämäläinen
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2010 – 2019
- 2019
- [j5]Wilhelmiina Hämäläinen, Geoffrey I. Webb:
A tutorial on statistically sound pattern discovery. Data Min. Knowl. Discov. 33(2): 325-377 (2019) - 2018
- [i5]Wilhelmiina Hämäläinen, Mike Joy, Florian Berger, Sami Huttunen:
Clustering students' open-ended questionnaire answers. CoRR abs/1809.07306 (2018) - 2017
- [c10]Wilhelmiina Hämäläinen, Geoffrey I. Webb:
Specious rules: an efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining. SDM 2017: 309-317 - [i4]Wilhelmiina Hämäläinen, Geoffrey I. Webb:
Specious rules: an efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining. CoRR abs/1709.03915 (2017) - 2016
- [j4]Wilhelmiina Hämäläinen:
New upper bounds for tight and fast approximation of Fisher's exact test in dependency rule mining. Comput. Stat. Data Anal. 93: 469-482 (2016) - 2014
- [c9]Wilhelmiina Hämäläinen, Geoffrey I. Webb:
Statistically sound pattern discovery. KDD 2014: 1976 - [c8]Wilhelmiina Hämäläinen, François Petitjean, Geoffrey I. Webb:
Preface to the 1st ECML/PKDD workshop on Statistically Sound Data Mining. SSDM@ECML/PKDD 2014: 1-2 - [e1]Wilhelmiina Hämäläinen, François Petitjean, Geoffrey I. Webb:
1st ECML/PKDD Workshop on Statistically Sound Data Mining, SSDM 2014, held at ECML/PKDD 2014, Nancy, France, September 15, 2014. JMLR Workshop and Conference Proceedings 47, JMLR.org 2014 [contents] - [i3]Wilhelmiina Hämäläinen:
New tight approximations for Fisher's exact test. CoRR abs/1405.1250 (2014) - [i2]Wilhelmiina Hämäläinen:
General upper bounds for well-behaving goodness measures on dependency rules. CoRR abs/1405.1339 (2014) - [i1]Wilhelmiina Hämäläinen:
Assessing the statistical significance of association rules. CoRR abs/1405.1360 (2014) - 2012
- [j3]Wilhelmiina Hämäläinen:
Kingfisher: an efficient algorithm for searching for both positive and negative dependency rules with statistical significance measures. Knowl. Inf. Syst. 32(2): 383-414 (2012) - [c7]Wilhelmiina Hämäläinen:
Thorough Analysis of Log Data with Dependency Rules: Practical Solutions and Theoretical Challenges. ICDM Workshops 2012: 579-586 - 2011
- [j2]Wilhelmiina Hämäläinen:
Efficient Search Methods for Statistical Dependency Rules. Fundam. Informaticae 113(2): 117-150 (2011) - [c6]Wilhelmiina Hämäläinen, Mikko Järvinen, Paula Martiskainen, Jaakko Mononen:
Jerk-based feature extraction for robust activity recognition from acceleration data. ISDA 2011: 831-836 - 2010
- [b1]Wilhelmiina Hämäläinen:
Efficient search for statistically significant dependency rules in binary data. University of Helsinki, Finland, 2010 - [j1]Wilhelmiina Hämäläinen:
StatApriori: an efficient algorithm for searching statistically significant association rules. Knowl. Inf. Syst. 23(3): 373-399 (2010) - [c5]Wilhelmiina Hämäläinen:
Efficient Discovery of the Top-K Optimal Dependency Rules with Fisher's Exact Test of Significance. ICDM 2010: 196-205
2000 – 2009
- 2009
- [c4]Wilhelmiina Hämäläinen:
Lift-based search for significant dependencies in dense data sets. KDD Workshop on Statistical and Relational Learning in Bioinformatics 2009: 12-16 - 2008
- [c3]Wilhelmiina Hämäläinen, Matti Nykänen:
Efficient Discovery of Statistically Significant Association Rules. ICDM 2008: 203-212 - 2006
- [c2]Wilhelmiina Hämäläinen, Mikko Vinni:
Comparison of Machine Learning Methods for Intelligent Tutoring Systems. Intelligent Tutoring Systems 2006: 525-534 - 2004
- [c1]Wilhelmiina Hämäläinen, Hannu Toivonen, Vladimir Poroshin:
Mining Relaxed Graph Properties in Internet. ICWI 2004: 152-159
Coauthor Index
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