Heikki Mannila

UAI Conference 2000 Conference Paper

Probabilistic Models for Query Approximation with Large Sparse Binary Data Sets

• Dmitry Pavlov
• Heikki Mannila
• Padhraic Smyth

TIME Conference 1997 Conference Paper

Similarity of Event Sequences

• Heikki Mannila
• Pirjo Ronkainen

Sequences of events are an important form of data that occurs in many application domains, such as telecommunications, biostatistics, user interface design, etc. We present a simple model for measuring the similarity of event sequences, and show that the resulting measure of distance can be efficiently computed using a form of dynamic programming.

ICML Conference 1996 Invited Paper

Data Mining and Machine Learning (Abstract)

• Heikki Mannila

TCS Journal 1995 Journal Article

Approximate inference of functional dependencies from relations

• Jyrki Kivinen
• Heikki Mannila

The functional dependency inference problem is the following. Given a relation r, find a set of functional dependencies that is equivalent with the set of all functional dependencies holding in r. All known algorithms for this task have running times that can be in the worst case exponential in the size of the smallest cover of the dependency set. We consider approximate dependency inference. We define various measures for the error of a dependency in a relation. These error measures have the value 0 if the dependency holds and a value close to 1 if the dependency clearly does not hold. Depending on the measure used, all dependencies with error at least ϵ in r can be detected with high probability by considering only O( 1 ε ) or O(|r| 1 2 /ε) random tuples of r. We also show how a machine learning algorithm due to Angluin et al. can be applied to give in output-polynomial time an approximately correct cover for the set of dependencies holding in r.