Active Learning for Constrained Document Clustering with Uncertainty Region
Constrained clustering is intended to improve accuracy and personalization based on the constraints expressed by an Oracle. In this paper, a new constrained clustering algorithm is proposed and some of the informative data pairs are selected during an iterative process. Then, they are presented to t...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/3207306 |
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| author | M. A. Balafar R. Hazratgholizadeh M. R. F. Derakhshi |
| author_facet | M. A. Balafar R. Hazratgholizadeh M. R. F. Derakhshi |
| author_sort | M. A. Balafar |
| collection | DOAJ |
| description | Constrained clustering is intended to improve accuracy and personalization based on the constraints expressed by an Oracle. In this paper, a new constrained clustering algorithm is proposed and some of the informative data pairs are selected during an iterative process. Then, they are presented to the Oracle and their relation is answered with “Must-link (ML) or Cannot-link (CL).” In each iteration, first, the support vector machine (SVM) is utilized based on the label produced by the current clustering. According to the distance of each document from the hyperplane, the distance matrix is created. Also, based on cosine similarity of word2vector of each document, the similarity matrix is created. Two types of probability (similarity and degree of similarity) are calculated and they are smoothed for belonging to neighborhoods. Neighborhoods form the samples that are labeled by Oracle, to be in the same cluster. Finally, at the end of each iteration, the data with a greater level of uncertainty (in term of probability) is selected for questioning the oracle. In order to evaluate, the proposed method is compared with famous state-of-the-art methods based on two criteria and over a standard dataset. The result demonstrates an increased accuracy and stability of the obtained result with fewer questions. |
| format | Article |
| id | doaj-art-a93bc96c11204491a3f0ede7140cd725 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a93bc96c11204491a3f0ede7140cd7252025-02-03T06:45:59ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/32073063207306Active Learning for Constrained Document Clustering with Uncertainty RegionM. A. Balafar0R. Hazratgholizadeh1M. R. F. Derakhshi2Department of IT, Faculty of Engineering, University of Tabriz, Tabriz, IranDepartment of IT, Faculty of Engineering, University of Tabriz, Tabriz, IranDepartment of Computer, Faculty of Engineering, University of Tabriz, Tabriz, IranConstrained clustering is intended to improve accuracy and personalization based on the constraints expressed by an Oracle. In this paper, a new constrained clustering algorithm is proposed and some of the informative data pairs are selected during an iterative process. Then, they are presented to the Oracle and their relation is answered with “Must-link (ML) or Cannot-link (CL).” In each iteration, first, the support vector machine (SVM) is utilized based on the label produced by the current clustering. According to the distance of each document from the hyperplane, the distance matrix is created. Also, based on cosine similarity of word2vector of each document, the similarity matrix is created. Two types of probability (similarity and degree of similarity) are calculated and they are smoothed for belonging to neighborhoods. Neighborhoods form the samples that are labeled by Oracle, to be in the same cluster. Finally, at the end of each iteration, the data with a greater level of uncertainty (in term of probability) is selected for questioning the oracle. In order to evaluate, the proposed method is compared with famous state-of-the-art methods based on two criteria and over a standard dataset. The result demonstrates an increased accuracy and stability of the obtained result with fewer questions.http://dx.doi.org/10.1155/2020/3207306 |
| spellingShingle | M. A. Balafar R. Hazratgholizadeh M. R. F. Derakhshi Active Learning for Constrained Document Clustering with Uncertainty Region Complexity |
| title | Active Learning for Constrained Document Clustering with Uncertainty Region |
| title_full | Active Learning for Constrained Document Clustering with Uncertainty Region |
| title_fullStr | Active Learning for Constrained Document Clustering with Uncertainty Region |
| title_full_unstemmed | Active Learning for Constrained Document Clustering with Uncertainty Region |
| title_short | Active Learning for Constrained Document Clustering with Uncertainty Region |
| title_sort | active learning for constrained document clustering with uncertainty region |
| url | http://dx.doi.org/10.1155/2020/3207306 |
| work_keys_str_mv | AT mabalafar activelearningforconstraineddocumentclusteringwithuncertaintyregion AT rhazratgholizadeh activelearningforconstraineddocumentclusteringwithuncertaintyregion AT mrfderakhshi activelearningforconstraineddocumentclusteringwithuncertaintyregion |