Mining Relaxed Graph Properties in Internet. - Wilhelmiina Hämäläinen; Hannu Toivonen; Vladimir Poroshin (ICWI, 2004)

Comparison of Machine Learning Methods for Intelligent Tutoring Systems. - Wilhelmiina Hämäläinen; Mikko Vinni (Intelligent Tutoring Systems, 2006)

Efficient Discovery of Statistically Significant Association Rules. - Wilhelmiina Hämäläinen; Matti Nykänen (ICDM, 2008)

Lift-based search for significant dependencies in dense data sets. - Wilhelmiina Hämäläinen (KDD Workshop on Statistical and Relational Learning in Bioinformatics, 2009)

Efficient Discovery of the Top-K Optimal Dependency Rules with Fisher's Exact Test of Significance. - Wilhelmiina Hämäläinen (ICDM, 2010)

StatApriori: an efficient algorithm for searching statistically significant association rules. - Wilhelmiina Hämäläinen (Knowl. Inf. Syst., 2010)

Efficient search for statistically significant dependency rules in binary data. - Wilhelmiina Hämäläinen (2010)

Jerk-based feature extraction for robust activity recognition from acceleration data. - Wilhelmiina Hämäläinen; Mikko Järvinen; Paula Martiskainen; Jaakko Mononen (ISDA, 2011)

Efficient Search Methods for Statistical Dependency Rules. - Wilhelmiina Hämäläinen (Fundam. Informaticae, 2011)

Thorough Analysis of Log Data with Dependency Rules: Practical Solutions and Theoretical Challenges. - Wilhelmiina Hämäläinen (ICDM Workshops, 2012)

Kingfisher: an efficient algorithm for searching for both positive and negative dependency rules with statistical significance measures. - Wilhelmiina Hämäläinen (Knowl. Inf. Syst., 2012)

Statistically sound pattern discovery. - Wilhelmiina Hämäläinen; Geoffrey I. Webb (KDD, 2014)

Preface to the 1st ECML/PKDD workshop on Statistically Sound Data Mining. - Wilhelmiina Hämäläinen; François Petitjean; Geoffrey I. Webb (SSDM@ECML/PKDD, 2014)

General upper bounds for well-behaving goodness measures on dependency rules. - Wilhelmiina Hämäläinen (CoRR, 2014)

Assessing the statistical significance of association rules. - Wilhelmiina Hämäläinen (CoRR, 2014)

New tight approximations for Fisher's exact test. - Wilhelmiina Hämäläinen (CoRR, 2014)

New upper bounds for tight and fast approximation of Fisher's exact test in dependency rule mining. - Wilhelmiina Hämäläinen (Comput. Stat. Data Anal., 2016)

Specious rules: an efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining. - Wilhelmiina Hämäläinen; Geoffrey I. Webb (SDM, 2017)

Clustering students' open-ended questionnaire answers. - Wilhelmiina Hämäläinen; Mike Joy; Florian Berger; Sami Huttunen (CoRR, 2018)

A tutorial on statistically sound pattern discovery. - Wilhelmiina Hämäläinen; Geoffrey I. Webb (Data Min. Knowl. Discov., 2019)

Optimizing Feature Selection for Unbalanced Metabolomics Data with A Background Factor: A Comparative Study in Parkinson's Disease. - Yinjia Zhang; Wilhelmiina Hämäläinen; Paavo Reinikka; Sanna-Kaisa Herukka; Ville Leinonen; Marko Lehtonen; Sárka Lehtonen (BIBM, 2024)

Clustering students' text form feedback data: comparison of eight vector space models. - Wilhelmiina Hämäläinen; Jan-Mikael Rybicki; Lauri Malmi; Alex Jung; Kerttu Pollari-Malmi (ICBDE, 2024)