Modelling Data Mining Dynamic Code Attributes With Scheme Definition Technique

Evasaria M Sipayung, Cut Fiarni, Randy Tanudjaja

Abstract


Data mining is a technique used in differentdisciplines to search for significant relationships among variablesin large data sets. One of the important steps on data mining isdata preparation. On these step, we need to transform complexdata with more than one attributes into representative format fordata mining algorithm. In this study, we concentrated on thedesigning a proposed system to fetch attributes from a complexdata such as product ID. Then the proposed system willdetermine the basic price of each product based on hiddenrelationships among the attributes of data. These researchesconclude that the proposed system accuracy of precision rate is98.7% and recall rate are 70.27%.

Keywords


data mining; attributes; recall; precision

References


T.Imielinski and H. Mannila. Communications of ACM. A database perspective on knowledge discovery, 39:58-64, 1996.

Gancheva, V. Market Basket Analysis of Beauty Products. Thesis on Erasmus University Rotterdam, 2013.

Yanrong Guo, Baoguo Wu, Yang Liu. Multidimensional Data Mining using a K-mean Algorithm based on the Forest Management Inventory of Fujian Province, China. TELKOMNIKA. 11(12):7290-7294, 2013.

Zhu, J.: Data Modeling for Big Data, CA, Beijing (2012).

B. Adelberg. NoDoSe: A tool for semi-automatically extracting structured and semi-structured data from text documents. In ACM International Conference on Management of Data (SIGMOD), 1998.

D. Angluin. On the complexity of minimum inference of regular sets. Information and Control, 39(3):337–350, 1978.

Hoda Waguih. A Data Mining Approach for the Detection of Denial of Service Attack. IAES International Journal of Artificial Intelligence, 2(2):99-106, 2013.


Full Text: PDF

Refbacks

  • There are currently no refbacks.