Abstract—In this paper, we will present an optimal algorithm by evaluation of effective parameters on data mining algorithm. Using effective parameters on interactions of clustering method, this algorithm, WKMSDI, increases speed of data recovery. It occurs when our qualification function can optimize or improve clustering scope with given parameters.
Attached parameters to basic function of k-means help us to increase system efficiency. For this purpose, recommended algorithm, compared to the preceding algorithms, increases system reliability for high processing. Effective parameters increase algorithm quality more than the compared algorithm. This needs integration of specific parameters to enhance system performance.
For enhancing quality in distributed integrated systems, we require the procedures which have direct access to some parameters. Usually, effective parameters increase system efficiency in very high interactions.
Index Terms—WKMSDI ; ERPSD ; ERPASD; Data Mining ; Knowledge Base ; Scheduling ; Dependability.
Payam Noor University, faculty of engineering, Tehran, Iran , email: a_ghorbannia@pnu.ac.ir
Islamic Azad University, Qazvin branch (qiau), qazvin , Iran , email: zeinali@qiau.ac.ir
Payam Noor University, faculty of engineering, Tehran,email:IranMfeiz78@gmail.com
Payam Noor University, faculty of engineering, Iran, Tehran, email: n.anisi@oiic-ir.com
Cite: Arash Ghorbannia Delavar, Esmaeil Zeynali Khesraghi, Majid Feizollahi, and Nasim Anisi, "WKMSDI: An Optimal Algorithm by Evaluation of Effective Parameters on Data Mining Algorithm," International Journal of Information and Education Technology vol. 1, no. 1, pp. 20-23, 2011.