| dc.description.abstract | In this task, some existing machine learning algorithms for compressing and decompressing texts
are analyzed to compare the performances of the algorithms and pick one for further work. This
study is done to develop a strategy like Heuristic Algorithm and collaborate with a peer to find a
solution to a text compression problem using the Text Compression Widget (Lossless
Compression Scheme).
Text compression is a process of reducing the size of the text by encoding it efficiently. In our
day to day life, sometimes we reach some points where the physical limit of a sent text or data
needs to be faster to work with. This large amount of information is needed to be compressed to
achieve a faster transformation. The ways to make text compression should be adaptive and
challengeable. A large data of English Language with high redundancy is used to apply the
techniques of compression and decompression. Compression of texts reduces the data storage
space. With the reduction or elimination of redundancies, data compression is accomplished. The
encoding technique uses fewer bits than the original one and unnecessary information is lost in
this case. It follows a lossless technique where the number of bits and bytes are cut off to store
the information and retrieve some of the physical limits. The decompression technique is a
reverse of the compression technique. Therefore, some factors like time, text size and type,
efficiency and throughput of an algorithm make the techniques challenging. | en_US |
