Concept Compression in Formal Concept Analysis Using Entropy-Based Attribute Priority
Discovering important concepts in formal concept analysis (FCA) is an important issue due to huge number of concepts arising out of complicated contexts. To address this issue, this paper proposes a method for concept compression in FCA, involving many-valued decision context, based on information e...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2017-03-01
|
| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2017.1316182 |
| _version_ | 1797684857009078272 |
|---|---|
| author | Sumangali K Aswani Kumar Ch. Jinhai Li |
| author_facet | Sumangali K Aswani Kumar Ch. Jinhai Li |
| author_sort | Sumangali K |
| collection | DOAJ |
| description | Discovering important concepts in formal concept analysis (FCA) is an important issue due to huge number of concepts arising out of complicated contexts. To address this issue, this paper proposes a method for concept compression in FCA, involving many-valued decision context, based on information entropy. The precedence order of attributes is obtained by using entropy theory developed by Shannon. The set of concepts is compressed using the precedence order thus determined. An algorithm namely Entropy based concept compression (ECC) is developed for this purpose. Further, similarity measures between the actual and compressed concepts are examined using the deviance analysis and percentage error calculation on the deviance of input weights of concepts. From the experiments, it is found that the compressed concepts inherit association rules to the maximum extent. |
| first_indexed | 2024-03-12T00:36:52Z |
| format | Article |
| id | doaj.art-14c01b98b2504ee4a8e252511e6f1388 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:36:52Z |
| publishDate | 2017-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-14c01b98b2504ee4a8e252511e6f13882023-09-15T09:33:55ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452017-03-0131325127810.1080/08839514.2017.13161821316182Concept Compression in Formal Concept Analysis Using Entropy-Based Attribute PrioritySumangali K0Aswani Kumar Ch.1Jinhai Li2VIT UniversityVIT UniversityKunming University of Science and TechnologyDiscovering important concepts in formal concept analysis (FCA) is an important issue due to huge number of concepts arising out of complicated contexts. To address this issue, this paper proposes a method for concept compression in FCA, involving many-valued decision context, based on information entropy. The precedence order of attributes is obtained by using entropy theory developed by Shannon. The set of concepts is compressed using the precedence order thus determined. An algorithm namely Entropy based concept compression (ECC) is developed for this purpose. Further, similarity measures between the actual and compressed concepts are examined using the deviance analysis and percentage error calculation on the deviance of input weights of concepts. From the experiments, it is found that the compressed concepts inherit association rules to the maximum extent.http://dx.doi.org/10.1080/08839514.2017.1316182 |
| spellingShingle | Sumangali K Aswani Kumar Ch. Jinhai Li Concept Compression in Formal Concept Analysis Using Entropy-Based Attribute Priority Applied Artificial Intelligence |
| title | Concept Compression in Formal Concept Analysis Using Entropy-Based Attribute Priority |
| title_full | Concept Compression in Formal Concept Analysis Using Entropy-Based Attribute Priority |
| title_fullStr | Concept Compression in Formal Concept Analysis Using Entropy-Based Attribute Priority |
| title_full_unstemmed | Concept Compression in Formal Concept Analysis Using Entropy-Based Attribute Priority |
| title_short | Concept Compression in Formal Concept Analysis Using Entropy-Based Attribute Priority |
| title_sort | concept compression in formal concept analysis using entropy based attribute priority |
| url | http://dx.doi.org/10.1080/08839514.2017.1316182 |
| work_keys_str_mv | AT sumangalik conceptcompressioninformalconceptanalysisusingentropybasedattributepriority AT aswanikumarch conceptcompressioninformalconceptanalysisusingentropybasedattributepriority AT jinhaili conceptcompressioninformalconceptanalysisusingentropybasedattributepriority |