Wilhelmiina Hämäläinen

According to our database, Wilhelmiina Hämäläinen authored at least 20 papers between 2004 and 2019.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2019

A tutorial on statistically sound pattern discovery.

Wilhelmiina Hämäläinen, Geoffrey I. Webb

Data Min. Knowl. Discov., 2019

2018

Clustering students' open-ended questionnaire answers.

Wilhelmiina Hämäläinen, Mike Joy, Florian Berger, Sami Huttunen

CoRR, 2018

2017

Specious rules: an efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining.

Wilhelmiina Hämäläinen, Geoffrey I. Webb

Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

2016

New upper bounds for tight and fast approximation of Fisher's exact test in dependency rule mining.

Wilhelmiina Hämäläinen

Comput. Stat. Data Anal., 2016

2014

New tight approximations for Fisher's exact test.

Wilhelmiina Hämäläinen

CoRR, 2014

Assessing the statistical significance of association rules.

Wilhelmiina Hämäläinen

CoRR, 2014

General upper bounds for well-behaving goodness measures on dependency rules.

Wilhelmiina Hämäläinen

CoRR, 2014

Preface to the 1st ECML/PKDD workshop on Statistically Sound Data Mining.

Wilhelmiina Hämäläinen, François Petitjean, Geoffrey I. Webb

Proceedings of the 1st ECML/PKDD Workshop on Statistically Sound Data Mining, 2014

Statistically sound pattern discovery.

Wilhelmiina Hämäläinen, Geoffrey I. Webb

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

2012

Kingfisher: an efficient algorithm for searching for both positive and negative dependency rules with statistical significance measures.

Wilhelmiina Hämäläinen

Knowl. Inf. Syst., 2012

Thorough Analysis of Log Data with Dependency Rules: Practical Solutions and Theoretical Challenges.

Wilhelmiina Hämäläinen

Proceedings of the 12th IEEE International Conference on Data Mining Workshops, 2012

2011

Efficient Search Methods for Statistical Dependency Rules.

Wilhelmiina Hämäläinen

Fundam. Informaticae, 2011

Jerk-based feature extraction for robust activity recognition from acceleration data.

Wilhelmiina Hämäläinen, Mikko Järvinen, Paula Martiskainen, Jaakko Mononen

Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, 2011

2010

Efficient search for statistically significant dependency rules in binary data.

Wilhelmiina Hämäläinen

PhD thesis, 2010

StatApriori: an efficient algorithm for searching statistically significant association rules.

Wilhelmiina Hämäläinen

Knowl. Inf. Syst., 2010

Efficient Discovery of the Top-K Optimal Dependency Rules with Fisher's Exact Test of Significance.

Wilhelmiina Hämäläinen

Proceedings of the ICDM 2010, 2010

2009

Lift-based search for significant dependencies in dense data sets.

Wilhelmiina Hämäläinen

Proceedings of the ACM SIGKDD Workshop on Statistical and Relational Learning in Bioinformatics, 2009

2008

Efficient Discovery of Statistically Significant Association Rules.

Wilhelmiina Hämäläinen, Matti Nykänen

Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008

2006

Comparison of Machine Learning Methods for Intelligent Tutoring Systems.

Wilhelmiina Hämäläinen, Mikko Vinni

Proceedings of the Intelligent Tutoring Systems, 8th International Conference, 2006

2004

Mining Relaxed Graph Properties in Internet.

Wilhelmiina Hämäläinen, Hannu Toivonen, Vladimir Poroshin

Proceedings of the IADIS International Conference WWW/Internet 2004, 2004