Identification and Evaluation of Functional Dependency Analysis using Rough sets for Knowledge Discovery

The process of data acquisition gained momentum due to the efficient representation of storage/retrieving systems. Due to the commercial and application value of these stored data, Database Management has become essential for the reasons like consistency and atomicity in giving birth to DBMS. The existing database management systems cannot provide the needed information when the data is not consistent. So knowledge discovery in databases and data mining has become popular for the above reasons. The non-trivial future expansion process can be classified as Knowledge Discovery. Knowledge Discovery process can be attempted by clustering tools. One of the upcoming tools for knowledge representation and knowledge acquisition process is based on the concept of Rough Sets. This paper explores inconsistencies in the existing databases by finding the functional dependencies extracting the required information or knowledge based on rough sets. It also discusses attribute reduction through core and reducts which helps in avoiding superfluous data. Here a method is suggested to solve this problem of data inconsistency based medical domain with a analysis.

@article{Sreevani2010,
  title     = {Identification and Evaluation of Functional Dependency Analysis using Rough sets for Knowledge Discovery},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {1},
  number    = {5},
  year      = {2010},
  publisher = {The Science and Information Organization},
  author    = {Y V Sreevani and Prof. T. Venkat Narayana Rao},
  doi       = {10.14569/IJACSA.2010.010510},
  url       = {https://doi.org/10.14569/IJACSA.2010.010510}
}