There is a growing need to analyse sets of complex data, i.e., data in which the individual data items are (semi-) structured collections of data themselves, such as sets of time-series. To perform such analysis, one has to redefine familiar notions such as similarity on such complex data types. One can do that either on the data items directly, or indi- rectly, based on features or patterns computed from the individual data items. In this paper, we argue that wavelet decomposition is a general tool for the latter approach

Springer
Lecture Notes in Artificial Intelligence
ESF Exploratory Workshop on Pattern Detection and Discovery
Database Architectures

Siebes, A., & Struzik, Z. R. (2002). Complex Data: Mining using Patterns. In Pattern Detection and Discovery: ESF Exploratory Workshop 2002 (0) (pp. 24–35). Springer.