2002

Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing

pdf

Sequence matching in time series databases is one of the mast important data mining applications. In this paper, we focus on subsequence matching. We propose an efficient approach to compare time series. To simpli@ the searching proem, we first use the KMP algorithm to carry through rough sequence matching. As KMP is a typical algorithm for string matching, we must transform time series into 0-1 string inspired by Literature [l]; then we quickly search all rough similar subsequences from major sequence and f i ~ l l yt,o reduce the dimension of raw time series data, we use Harr wavelet transform to represent the sequence to be compared and use WT (Wavelet Transformations) coefficients to compute the similarity of two sequences. That we carry out rough matching at first may reduce the numbers of WT and quicken the whole subsequence matching process.

EA 18

1397/10/01

Dashagha

19