Shaykhian, G. A., & Khairi, M. A. (2014, June), Factors Influencing Data Management Models Selection  Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--20484

\bibitem{asee_peer_20484}
  Shaykhian, G. A., \& Khairi, M. A. (2014, June), \emph{Factors Influencing Data Management Models Selection}
  Paper presented at 2014 ASEE Annual Conference \& Exposition, Indianapolis, Indiana.
  10.18260/1-2--20484

Gholam Ali Shaykhian and Mohd Abdelgadir Khairi.  "Factors Influencing Data Management Models Selection".  2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana, 2014, June.  ASEE Conferences, 2014.  https://peer.asee.org/20484 Internet.  15 Dec, 2024

\bibitem{asee_peer_20484}
  Gholam Ali Shaykhian and Mohd Abdelgadir Khairi.
  "Factors Influencing Data Management Models Selection".
  \emph{2014 ASEE Annual Conference \& Exposition, Indianapolis, Indiana, 2014, June}.
  ASEE Conferences, 2014.
  https://peer.asee.org/20484 Internet.  15 Dec, 2024

@INPROCEEDINGS{asee_peer_20484
author = "Gholam Ali Shaykhian and Mohd Abdelgadir Khairi"
title = "Factors Influencing Data Management Models Selection"
booktitle = "2014 ASEE Annual Conference \& Exposition"
year = "2014"
month = "June"
address = "Indianapolis, Indiana"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/20484}
number = {10.18260/1-2--20484}
}

TY  - CPAPER
AB  -          Improve Data Mining and Knowledge Discovery: Data Analysis Tools                                 and Algorithms                                            Abstract        Data mining is widely used to mine business, engineering, and scientific data. Datamining uses pattern based queries, searches, or other analyses of one or more electronicdatabases/datasets in order to discover or locate a predictive pattern or anomaly indicative ofsystem failure, criminal or terrorist activity. There are various algorithms, techniques andmethods used to mine data; including neural networks, genetic algorithms, decision trees, neatestneighbor method, rule induction association analysis, slice and dice, segmentation, andclustering. These algorithms, techniques and methods each uniquely detects patterns in a dataset,have been instrumentals in the development of numerous open source and commerciallyavailable products and technology for data mining.        Data mining is best realized when latent information in a large quantity of data stored isdiscovered. No one technique solves all data mining problems; challenges are to selectalgorithms or methods appropriate to strengthen data/text mining and trending within a givendatasets. In recent years, throughout industry, academia and government agencies, thousands ofdata systems are designed and tailored to serve specific engineering and business needs. Many ofthese systems use databases with relational algebra and structured query language to categorizeand retrieve data. In these systems, data analyses are limited and require prior explicit knowledgeof metadata and database relations; lacking exploratory data mining and discoveries of latentinformation.        This presentation introduces MatLab® (MATrix LABoratory), an engineering andscientific data analyses tool to perform data mining. MatLab was originally intended to performpurely numerical calculations (a glorified calculator). Now, in addition to having hundreds ofmathematical functions, it is a programming language with hundreds built in standard functionsand numerous available toolboxes. MatLab’s ease of data processing, visualization and itsenormous availability of built in functionalities and toolboxes make it suitable to performnumerical computations and simulations as well as a data mining tool. Engineers and scientistscan take advantage of the readily available functions/toolboxes to gain wider insight in theirperspective data mining experiments.
AU  - Gholam Ali Shaykhian
AU  - Mohd Abdelgadir Khairi
CY  - Indianapolis, Indiana
DA  - 2014/06/15
PB  - ASEE Conferences
TI  - Factors Influencing Data Management Models Selection
UR  - https://peer.asee.org/20484
DO  - 10.18260/1-2--20484
ER  -