Dynamic Clustering for Information Retrieval from Big Data Depending on Compressed Files

The rapid growth in the database data led to origination a large amount of data. So, it is still a big problem to access this data for answering user queries. In this paper a novel approach for aggregating the required data was proposed, this approach called dynamic clustering. Also, several retrieval methods were used for retrieving purposes. The dynamic clustering method is built clusters according to the user entries (queries). It has been applied to different compressed database files in different size and using different queries. The compressed database file it is resulted from applying ICM (Ideal Compression Method) and best compressed algorithm(improved k-mean, k-mean with medium probability and k-mean with maximum gain ratio).The retrieval methods applied to original database file, compressed file and the cluster that result from implementing dynamic clustering algorithm and the results was compared.

@article{F.2016,
  title     = {Dynamic Clustering for Information Retrieval from Big Data Depending on Compressed Files},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {1},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Dr.Alaa Kadhim F. and Prof. Dr. Ghassan H. Abdul and Rasha Subhi Ali},
  doi       = {10.14569/IJACSA.2016.070140},
  url       = {https://doi.org/10.14569/IJACSA.2016.070140}
}