Abstract
All present techniques for mining data stream can detect concept drift, outlier and pattern by using separate techniques directly on data stream and an applicable single technique from a higher level of abstraction for detecting all these has not been developed. The aim here is to develop a technique which can detect concept drift, outlier and pattern using a single model. In order to achieve this goal, we are switching from the traditional thinking of applying all techniques directly on data stream. Here, we have focused on the class labels of the data which is found using classification. This technique gives us a higher level of abstraction of the data and consumes a lower amount of memory. Moreover, we are proposing the idea to keep track of very old data using a bit table. Our technique can also store the timestamp of the pattern or drift.
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Abdullah-Al-Mamun, Anowarul Abedin, M., Al Arman, M., Mottalib, M.A., Huq, M.R. (2011). Mining Data Stream from a Higher Level of Abstraction: A Class Window Approach. In: Abd Manaf, A., Sahibuddin, S., Ahmad, R., Mohd Daud, S., El-Qawasmeh, E. (eds) Informatics Engineering and Information Science. ICIEIS 2011. Communications in Computer and Information Science, vol 254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25483-3_38
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DOI: https://doi.org/10.1007/978-3-642-25483-3_38
Publisher Name: Springer, Berlin, Heidelberg
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