<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">A modified Fisher discriminate analysis method for classifying stream data is presented. To satisfy the realtime demand in classifying stream data, this method defines a new criterion for Fisher discriminate analysis. Since the new criterion requires less computation and memory space, it is much faster and more suitable for online processing in stream data environment. It can overcome the problem of singular within-class scatter matrix in traditional FDA. Our algorithm speeds up the mining process while maintaining the high classification accuracy and capturing the up-todate trends in the stream. Experiments on real and synthetic data sets show that our algorithm can improve the classification accuracy and speed for stream data classification.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"></span></p>
Searching for the longest common substring (LCS) of biosequences is one of the most important tasks in Bioinformatics. A fast algorithm for LCS problem named FAST_LCS is presented. The algorithm first seeks the successors of the initial identical character pairs according to a successor table to obtain all the identical pairs and their levels. Then by tracing back from the identical character pair at the largest level, the result of LCS can be obtained. For two sequences X and Y with lengths n and m, the memory required for FAST_LCS is max{8*(n+1)+8*(m+1),L}, here L is the number of identical character pairs and time complexity of parallel implementation is O(|LCS(X,Y)|), here, |LCS(X,Y)| is the length of the LCS of X,Y. Experimental result on the gene sequences of tigr database shows that our algorithm can get exactly correct result and is faster and more efficient than other LCS algorithms.
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