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File size is a measure of how much data a computer file contains or how much storage space it is allocated. Typically, file size is expressed in units based on byte . A large value is often expressed with a metric prefix (as in megabyte and gigabyte ) or a binary prefix (as in mebibyte and gibibyte ).
Thus, a representation that compresses the storage size of a file from 10 MB to 2 MB yields a space saving of 1 - 2/10 = 0.8, often notated as a percentage, 80%. For signals of indefinite size, such as streaming audio and video, the compression ratio is defined in terms of uncompressed and compressed data rates instead of data sizes:
34,359,738,368 bits (4 gibibytes) – maximum addressable memory for the Motorola 68020 (1984) and Intel 80386 (1985), also the volume size limit for the FAT16B file system (with 64 KiB clusters) as well as the maximum file size (4 GiB-1) in MS-DOS 7.1-8.0. 3.76 × 10 10 bits (4.7 gigabytes) – capacity of a single-layer, single-sided DVD: 2 36
On the other hand, a hard disk whose capacity is specified by the manufacturer as "10 gigabytes" or "10 GB", holds 10 × 10 9 = 10 000 000 000 bytes, or a little more than that, but less than 10 × 2 30 = 10 737 418 240 and a file whose size is listed as "2.3 GB" may have a size closer to 2.3 × 2 30 ≈ 2 470 000 000 or to 2.3 × 10 9 = 2 300 ...
A typical method of performing a measurement is to transfer a 'large' file from one system to another system and measure the time required to complete the transfer or copy of the file. The throughput is then calculated by dividing the file size by the time to get the throughput in megabits, kilobits, or bits per second.
Bit Calculator – a tool providing conversions between bit, byte, kilobit, kilobyte, megabit, megabyte, gigabit, gigabyte; BitXByteConverter Archived 2016-04-06 at the Wayback Machine – a tool for computing file sizes, storage capacity, and digital information in various units
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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.