An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
Essential wavelets for statistical applications and data analysis
Essential wavelets for statistical applications and data analysis
Principles of data mining
Exploratory image databases: content-based retrieval
Exploratory image databases: content-based retrieval
The Haar Wavelet Transform in the Time Series Similarity Paradigm
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Efficient Index Structures for String Databases
Proceedings of the 27th International Conference on Very Large Data Bases
IEEE Transactions on Information Theory
Information shared by many objects
Proceedings of the 17th ACM conference on Information and knowledge management
New information distance measure and its application in question answering system
Journal of Computer Science and Technology
Features for learning local patterns in time-stamped data
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
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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 indirectly, 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.