Knowledge Discovery from Sequential Data
Frank Höppner · Digitale Bibliothek Braunschweig (Verbundzentrale Göttingen (VZG)) · 2003
A new framework for analyzing sequential or temporal data such as time series is proposed. It differs from other approaches by the special emphasis on the interpretability of the results, since interpretability is of vital importance for knowledge discovery, that is, the development of new knowledge (in the head of a human) from a list of discovered patterns. While traditional approaches try to model and predict all time series observations, the focus in this work is on modelling local dependencies in multivariate time series. This makes it possible to deal with irregular or chaotic series. The proposed discovery process consists of (1) time series abstraction to get a representation close to the human perception of time series, (2) the enumeration and ranking of qualitative relationships in the data, (3) the specialization with quantitative constraints and the generalization of patterns to overcome limitations that are implicitly induced by the search bias.