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Dimension diminution of Data objects: A survey
Nootan Agrawal,  Mr. Sandeep Gonnade
Data mining application has massive advantages, as historical data have huge number of features. Feature selection is an essential role in improving the eminence of learning algorithms in data mining and machine. This has been broadly deliberated in supervised learning, whereas it is still comparatively infrequent researched in case of unsupervised learning. Each data mining application has familiar matter; dataset has huge number of features which is immaterial or redundant to the data mining job in hand which pessimistically affects the performance of the fundamental learning algorithms, and makes them less efficient. Henceforth reducing the dimensionality of dataset is primary and important job for data mining applications and machine learning algorithms so that computational burden of the learning algorithms can be minimized. In this paper we will discuss different feature selection algorithms so as to find out factors which affect the performance of existing algorithm so that we can move further for researching another novel method for data mining application.
Keywords- Supervised learning, Unsupervised learning, Feature selection
Unique Identification Number - IJEDR1603015Page Number(s) - 82-86Pubished in - Volume 4 | Issue 3 | July 2016DOI (Digital Object Identifier) -    Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
Nootan Agrawal,  Mr. Sandeep Gonnade,   "Dimension diminution of Data objects: A survey"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.4, Issue 3, pp.82-86, July 2016, Available at :http://www.ijedr.org/papers/IJEDR1603015.pdf