C-W-FCM: Constrained Weighted Fuzzy Clustering Algorithm with a Semi-Supervised Approach for Text Classification

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.

Ramezani Pour Soheila; Naderan Marjan; Mortazavi Saeid Allah

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

Journal Paper

Paper Information

Journal: THE CSI JOURNAL ON COMPUTER SCIENCE AND ENGINEERING Year:2019 | Volume:16 | Issue:2 Page(s): 14-29

Download Full-Text

View:

143

Download:

124

Cites:

Information Journal Paper

Title

C-W-FCM: Constrained Weighted Fuzzy Clustering Algorithm with a Semi-Supervised Approach for Text Classification

Author(s)

Ramezani Pour Soheila | Naderan Marjan | Mortazavi Saeid Allah | Issue Writer Certificate

Keywords

Text classification

fuzzy clustering

semi-supervised

genetic algorithm

Abstract

The emergence of digital information era and rapid development of the Internet makes information to change gradually from paper form to the electronic one. This makes the users capable to search the news and books in an electronic way. Thus, the existence of systems for information retrieval appears to be essential. This paper suggests a system for Text classification by means of semi-supervised fuzzy clustering with a weighted feature vector. In the proposed method, after a preprocessing phase, a genetic algorithm together with the TF-IDF method is used for dimensionality reduction. Accordingly, features with highest discriminating power are chosen and finally, the documents are classified with the clustering algorithm, C-W-FCM. In fact, the proposed clustering algorithm applies the Euclidean distance with different weights for different dimensions. For evaluation of the proposed approach, a number of prominent criteria for clustering, namely Fukuyama and Sugeno (FS), are used conducted on the Reuters dataset. It is assumed that a small number of documents have labels which are called the seeded set. Simulation results show that the proposed approach is 27 to 33% superior to conventional clustering algorithms based on the evaluation criteria in determining clusters. In addition, the proposed clustering algorithm increases the system effectiveness especially when documents are highly similar to each other.

Cites

No record.

References

No record.

Cite

APA: Copy

Ramezani Pour, Soheila, Naderan, Marjan, & Mortazavi, Saeid Allah. (2019). C-W-FCM: Constrained Weighted Fuzzy Clustering Algorithm with a Semi-Supervised Approach for Text Classification. THE CSI JOURNAL ON COMPUTER SCIENCE AND ENGINEERING, 16(2), 14-29. SID. https://sid.ir/paper/776180/en

Vancouver: Copy

Ramezani Pour Soheila, Naderan Marjan, Mortazavi Saeid Allah. C-W-FCM: Constrained Weighted Fuzzy Clustering Algorithm with a Semi-Supervised Approach for Text Classification. THE CSI JOURNAL ON COMPUTER SCIENCE AND ENGINEERING[Internet]. 2019;16(2):14-29. Available from: https://sid.ir/paper/776180/en

IEEE: Copy

Soheila Ramezani Pour, Marjan Naderan, and Saeid Allah Mortazavi, “C-W-FCM: Constrained Weighted Fuzzy Clustering Algorithm with a Semi-Supervised Approach for Text Classification,” THE CSI JOURNAL ON COMPUTER SCIENCE AND ENGINEERING, vol. 16, no. 2, pp. 14–29, 2019, [Online]. Available: https://sid.ir/paper/776180/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops