Attribute, Event Sequence, and Event Type Similarity Notions for Data Mining

In data mining and knowledge discovery, similarity between objects is one of the central concepts. A measure of similarity can be user-de ned, but an important problem is de ning similarity on the basis of data. In this thesis we consider three kinds of similarity notions: similarity between binary attributes, similarity between event sequences, and similarity between event types occurring in sequences. Traditional approaches for de ning similarity between two attributes typically consider only the values of those two attributes, not the values of any other attributes in the relation. Such similarity measures are often useful, but unfortunately they cannot describe all important types of similarity. Therefore, we introduce a new attribute similarity measure that takes into account the values of other attributes in the relation. The behavior of the di erent measures of attribute similarity is demonstrated by giving empirical results on two real-life data sets. We also present a simple model for de ning similarity between event sequences. This model is based on the idea that a similarity notion should re ect how much work is needed in transforming an event sequence into another. We formalize this notion as an edit distance between sequences. Then we show how the resulting measure of distance can be e ciently computed using a form of dynamic programming, and also give some experimental results on two real-life data sets. As the third case of similarity notions, we study how similarity between types of events occurring in sequences could be de ned. Intuitively, two