Abstract: Data Mining, or knowledge discovery, is the computer-assisted process of digging through and analyzing large quantity of data in order to extract meaningful knowledge. Data mining methods are used in many studies to identify phenomena quicker and better than human experts. One class of these methods was designed for dealing with time series data. However, when several channels of data are collected simultaneously, data mining algorithms encounter numerous difficulties since channels may be measured in different units, may be recorded at different sampling-rates, or may have completely different characteristics. Furthermore, as the size of these data increases, the amount of irrelevant data usually increases as well and the process becomes impractical. Hence, in such cases, the analyst must be capable of focusing on the informational parts while ignoring the noise data. These kinds of difficulties complicate the analysis of multichannel data as compared to the analysis of single-channel data. This paper presents a useful technique for preprocessing multi channel data. Our technique supplies tools for coping with all the above-mentioned difficulties, and prepares the data for further analysis (using common algorithms, especially from the data mining field).
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