BibTeX:

@article{IJIRSTV3I1086,
     title={Analysis of the Behaviour of Reduced and Compressed Data with Various Learning Algorithms },
     author={Swati Kaur},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={1},
     pages={153--160},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I1086.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

In machine learning and statistics, classification is issue of recognizing to which of an arrangement of classes a new observation belongs, on the basis of training set of information containing perceptions (or cases) whose classification enrollment is known. Progresses in information collection and storage capabilities amid the previous decades have prompted a data burden in many scientific areas. In this paper analysis of the performance of various classification algorithm is done on reduced and compressed data and concluded which data is more effective for classifying the data. Low Rank matrices perform the best among the three and Huffman’s coding is preferred when we want to save memory space and AANNs is efficient when it to reduce the dimensionality of the data. For classification Low Rank Matrices produces more accurate result than other two.

Dimension reduction, Data mining, data compression, auto associative neural network, Huffman’s coding, Low rank Matrices