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Data compression aims to reduce the size of data files, enhancing storage efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the centroid of its points.
Lossless compression of digitized data such as video, digitized film, and audio preserves all the information, but it does not generally achieve compression ratio much better than 2:1 because of the intrinsic entropy of the data. Compression algorithms which provide higher ratios either incur very large overheads or work only for specific data ...
Using a statistical description for data, information theory quantifies the number of bits needed to describe the data, which is the information entropy of the source. Data compression (source coding): There are two formulations for the compression problem: lossless data compression: the data must be reconstructed exactly;
When applied to two nodes in a network whose data are in close range of each other modulo-N code requires one node (say odd) to send the coded data value as the raw data =; the even node is required to send the coded data as the =. Hence the name modulo-N code.
Data compression which explicitly tries to minimize the average length of messages according to a particular assumed probability model is called entropy encoding. Various techniques used by source coding schemes try to achieve the limit of entropy of the source.
Van Jacobson Header Compression (also VJ compression, or just Header Compression) is an option in most versions of PPP. Versions of Serial Line Internet Protocol (SLIP) with VJ compression are often called CSLIP (Compressed SLIP).
The presentation layer handles protocol conversion, data encryption, data decryption, data compression, data decompression, incompatibility of data representation between operating systems, and graphic commands. The presentation layer transforms data into the form that the application layer accepts, to be sent across a network.
In information theory, the source coding theorem (Shannon 1948) [2] informally states that (MacKay 2003, pg. 81, [3] Cover 2006, Chapter 5 [4]): N i.i.d. random variables each with entropy H(X) can be compressed into more than N H(X) bits with negligible risk of information loss, as N → ∞; but conversely, if they are compressed into fewer than N H(X) bits it is virtually certain that ...