Abstract
Rapid development in data analysis domain causes an ever growing demand for Market Basket Analysis. However, predefined methods in this domain emphasize on different techniques which concentrate to select appropriate items. In this paper, we tried to develop a framework for cleaning the dataset that depends on the proposition that “Better noise removal brings out better data analysis”. Eliminating noisy objects is an essential goal of data preprocessing as noise hampers data analysis. Data cleaning techniques which are recently developed concentrates on noise removals that are the consequences of low-level data errors. It causes due to defective data gathering process, but data objects that are clearly connected or related only at some particular time or unrelated/unimportant can also be significantly interfere with data analysis. Thus, in order to improve the data analysis to a greater extent, noisy data with respect to the underlying analysis must be removed at data preprocessing which is one of the steps of Knowledge Discovery in Databases (KDD). Hence to remove all types of noise, there is a need of data cleaning strategies. Because data sets can contain enormous measures of noise, these methods also need to be able to remove extensive portion of the data. To augment data analysis in existence of high noise intensity, this paper find method meant for noise removal.
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Gangurde, R., Kumar, B., Gore, S.D. (2018). Noise Removal Framework for Market Basket Analysis. In: Deshpande, A., et al. Smart Trends in Information Technology and Computer Communications. SmartCom 2017. Communications in Computer and Information Science, vol 876. Springer, Singapore. https://doi.org/10.1007/978-981-13-1423-0_24
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DOI: https://doi.org/10.1007/978-981-13-1423-0_24
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