The Application and Analysis of Data Mining in Clustering Data of Petroleum Pipeline

Abstract

dopting the methods of the K-means and the SOFM neutral network in the data mining and basing on the characteristics of data of petroleum pipeline, a system is built that fits for the data mining and the evaluation on the effects of the clustering data. It is shown that the effects of this data mining are the best by comparison between the two results of the clustering data by using the multiple regression analysis. Keywords-Data mining, K-means, SOFM neutral network, Multiple regression analysis. I. INTRODUCTION The data mining makes the database technique enter into a higher stage. It can not only query and ergodic the past data but also find out the potential connections between the past data. Thus it can promote the transmission of the information. Today, data mining technology is mainly applied in the classifying, clustering and forecasting areas(1). This paper adopts the clustering K-means and SOFM neutral networks. In the oil and gas industry, pipeline transport grows rapidly in the present worldwide. There is an obvious advantage of the pipeline transport over others in the transportation of the oil and gas. The oil transport is an important link in the normal operation of this industry, and it preserves large amount of statistics as well, which provides a great deal of information for the analysis of the whole oil transportation(2). So, analyzing the data of petroleum pipeline by using the clustering algorithm in the data mining is helpful to the thorough research on the fault identification of the pipeline, diagnosis and forecast, the safety of the transport, the response to accidents, the price making after interconnection reconstruction.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.2991/iccasm.2012.392 under the license https://creativecommons.org/licenses/by-nc

• https://download.atlantis-press.com/article/2856.pdf

• https://www.atlantis-press.com/proceedings/iccasm-12/2856,

https://academic.microsoft.com/#/detail/2035091324 under the license cc-by-nc