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In this paper, we developed a new method that progressively construct and update a set of alignments by adding sequences in certain order to each of the existing alignments. Each of the existing alignments is modelled with a profile Hidden Markov Model (HMM) and an added sequence is aligned to each of these profile HMMs. We introduced an integer parameter for the number of profile HMMs. The profile HMMs are then updated based on the alignments with leading scores. Our experiments on BaliBASE showed that our approach could efficiently explore the alignment space and significantly improve the alignment accuracy.

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In ubiquitous environment, too much information is generated from a lot of sensors, and people want to obtain the appropriately classified information from the information. Decision tree algorithm like C4.5 is very useful in the field of data mining or machine learning system. Because this is fast and deduces good result on the problem of classification. This paper proposes three methods using decision tree for solving a classification problem.Firstly, this paper suggest about the extended data expression for explaining a classifier, Uchoo. Secondly, a classifier, UChoo, is described. Thirdly, this paper is to describe about rule generation. The rules expressed in the newly suggested method have almost the same information contents as compared with the original data set. The information is gotten from the sensors becomes large amount of data as the ubiquitous computation environment develops, therefore it is impossible to keep all information in memory. However, using suggested method, this problem is solved smoothly with having almost the information.