Image compression techniques are the most apprehensive topics in today"s high-tech environment. Singular Value Decomposition (SVD) is one of the image compression technique. SVD is an attractive algebraic transform for digital image processing applications. The SVD method can transform matrix A into product, which allows us to refactor a digital image in three orthogonal matrices. The using of singular values of such refactoring allows us to represent the image with a reduced set of values, which can store the useful features of the given original image, also use less storage space of the memory, and achieve the image compression process. In this paper, discuss how SVD is applied to images, the technique of image compression and maintain the quality of the image using SVD and also the algorithm to compress an image using MATLAB.

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Due to increasing size of text and audio data over internet, various techniques are needed to help with the finding and extraction of very specific information relevant to a user's task. Text mining is a variant on a field called data mining that tries to discover curious patterns from large databases. Singular value decomposition this technique is used for dimensionality reduction of large database on Apache MAHOUT Hadoop framework. In this paper, different existing Information Retrieval, Pattern Matching, Rule Generation Algorithm is reviewed. In addition for extracting questionnaires and curiosity based sentences from large database some different implementation of the algorithms is proposed. Finally the extract user required information from large unstructured database.