MODIFICATION OF ROCK MASS RATING CLASSIFICATION SYSTEM BY K-MEANS AND FUZZY C-MEANS CLUSTERING ALGORITHMS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

JALALI Z.; MOUSAVI NASAB S.M.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF ANALYTICAL AND NUMERICAL METHODS IN MINING ENGINEERING Year:2015 | Volume:5 | Issue:9 Page(s): 73-84

Download Full-Text

Persian Verion

View:

1,230

Download:

Cites:

Information Journal Paper

Title

MODIFICATION OF ROCK MASS RATING CLASSIFICATION SYSTEM BY K-MEANS AND FUZZY C-MEANS CLUSTERING ALGORITHMS

Author(s)

JALALI Z. | MOUSAVI NASAB S.M. | Issue Writer Certificate

Keywords

ROCK MASS RATING CLASSIFICATION SYSTEM

K-MEANS AND FCM CLUSTERING ALGORITHMS

CLUSTERING VALIDATION METHODS

ANOMALY B OF SANGAN IRON MINES

Abstract

Given the importance of the ROCK MASS RATING CLASSIFICATION SYSTEM in rock engineering, the aim of this paper is to improve final classes of this classification system using k-means and fuzzy c-means clustering algorithms. The data classification in the ROCK MASS RATING CLASSIFICATION SYSTEM were allocated to certain classes via a set of initial information based on the opinions and judgments of experience, which the use of clustering algorithms in this system of classification, dataset were divided into specific classes after going through the stages of clustering analysis, therefore resulting in clarification of the final ROCK MASS RATING CLASSIFICATION SYSTEMs and removal of uncertainties from the linguistic criteria. Silhouette coefficient (SC) method was used for validation k-means clustering algorithm. Furthermore, for validation of FCM clustering algorithm, four validation methods including partition distribution coefficient (PC), clustering entropy (CE), Fukuyama and Sugeno (FS) and Xie and Beni index (XB) were used. It becomes clear that due to uncertainty condition on determination of rock mass rating clustering system classes, FCM clustering algorithms yields better results than k-means clustering algorithm. Results of data extracted from ANOMALY B OF SANGAN IRON MINES indicated that the technique used in this paper is of high importance in rock mass quality.

Cites

No record.

References

No record.

Cite

APA: Copy

JALALI, Z., & MOUSAVI NASAB, S.M.. (2015). MODIFICATION OF ROCK MASS RATING CLASSIFICATION SYSTEM BY K-MEANS AND FUZZY C-MEANS CLUSTERING ALGORITHMS. JOURNAL OF ANALYTICAL AND NUMERICAL METHODS IN MINING ENGINEERING, 5(9 ), 73-84. SID. https://sid.ir/paper/266027/en

Vancouver: Copy

JALALI Z., MOUSAVI NASAB S.M.. MODIFICATION OF ROCK MASS RATING CLASSIFICATION SYSTEM BY K-MEANS AND FUZZY C-MEANS CLUSTERING ALGORITHMS. JOURNAL OF ANALYTICAL AND NUMERICAL METHODS IN MINING ENGINEERING[Internet]. 2015;5(9 ):73-84. Available from: https://sid.ir/paper/266027/en

IEEE: Copy

Z. JALALI, and S.M. MOUSAVI NASAB, “MODIFICATION OF ROCK MASS RATING CLASSIFICATION SYSTEM BY K-MEANS AND FUZZY C-MEANS CLUSTERING ALGORITHMS,” JOURNAL OF ANALYTICAL AND NUMERICAL METHODS IN MINING ENGINEERING, vol. 5, no. 9 , pp. 73–84, 2015, [Online]. Available: https://sid.ir/paper/266027/en

Related Journal Papers