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This is a CSS framework designed to cut down on your CSS development time. It gives you a solid foundation to build your own CSS on

Sax: Symbolic Aggregate approXimation -- SAX is the first symbolic representation for time series that allows for dimensionality reduction and indexing with a lower-bounding distance measure. In classic data mining tasks such as clustering, classification, index, etc., SAX is as good as well-known representations such as Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT), while requiring less storage space. In addition, the representation allows researchers to avail of the wealth of data structures and algorithms in bioinformatics or text mining, and also provides solutions to many challenges associated with current data mining tasks. One example is motif discovery, a problem which we recently defined for time series data. There is great potential for extending and applying the discrete representation on a wide class of data mining tasks. Source code has "non-commercial" license

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