Unexpected rules using a conceptual distance based on fuzzy ontology
One of the major drawbacks of data mining methods is that they generate a notably large number of rules that are often obvious or useless or, occasionally, out of the user’s interest. To address such drawbacks, we propose in this paper an approach that detects a set of unexpected rules in a discover...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2014-01-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157813000141 |
| _version_ | 1818539416609619968 |
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| author | Mohamed Said Hamani Ramdane Maamri Yacine Kissoum Maamar Sedrati |
| author_facet | Mohamed Said Hamani Ramdane Maamri Yacine Kissoum Maamar Sedrati |
| author_sort | Mohamed Said Hamani |
| collection | DOAJ |
| description | One of the major drawbacks of data mining methods is that they generate a notably large number of rules that are often obvious or useless or, occasionally, out of the user’s interest. To address such drawbacks, we propose in this paper an approach that detects a set of unexpected rules in a discovered association rule set. Generally speaking, the proposed approach investigates the discovered association rules using the user’s domain knowledge, which is represented by a fuzzy domain ontology. Next, we rank the discovered rules according to the conceptual distances of the rules. |
| first_indexed | 2024-12-11T21:41:45Z |
| format | Article |
| id | doaj.art-3c7897bc69194f2a971f6766a375a649 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-12-11T21:41:45Z |
| publishDate | 2014-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-3c7897bc69194f2a971f6766a375a6492022-12-22T00:49:48ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782014-01-012619910910.1016/j.jksuci.2013.06.001Unexpected rules using a conceptual distance based on fuzzy ontologyMohamed Said Hamani0Ramdane Maamri1Yacine Kissoum2Maamar Sedrati3Lire Laboratory, University of M’sila, AlgeriaLire Laboratory, University of Constantine 2, AlgeriaLire Laboratory, University of Skikda, AlgeriaUniversity of Hadj Lakhdar Batna, AlgeriaOne of the major drawbacks of data mining methods is that they generate a notably large number of rules that are often obvious or useless or, occasionally, out of the user’s interest. To address such drawbacks, we propose in this paper an approach that detects a set of unexpected rules in a discovered association rule set. Generally speaking, the proposed approach investigates the discovered association rules using the user’s domain knowledge, which is represented by a fuzzy domain ontology. Next, we rank the discovered rules according to the conceptual distances of the rules.http://www.sciencedirect.com/science/article/pii/S1319157813000141Fuzzy ontologyUnexpectednessAssociation ruleDomain knowledgeInterestingnessConceptual distance |
| spellingShingle | Mohamed Said Hamani Ramdane Maamri Yacine Kissoum Maamar Sedrati Unexpected rules using a conceptual distance based on fuzzy ontology Journal of King Saud University: Computer and Information Sciences Fuzzy ontology Unexpectedness Association rule Domain knowledge Interestingness Conceptual distance |
| title | Unexpected rules using a conceptual distance based on fuzzy ontology |
| title_full | Unexpected rules using a conceptual distance based on fuzzy ontology |
| title_fullStr | Unexpected rules using a conceptual distance based on fuzzy ontology |
| title_full_unstemmed | Unexpected rules using a conceptual distance based on fuzzy ontology |
| title_short | Unexpected rules using a conceptual distance based on fuzzy ontology |
| title_sort | unexpected rules using a conceptual distance based on fuzzy ontology |
| topic | Fuzzy ontology Unexpectedness Association rule Domain knowledge Interestingness Conceptual distance |
| url | http://www.sciencedirect.com/science/article/pii/S1319157813000141 |
| work_keys_str_mv | AT mohamedsaidhamani unexpectedrulesusingaconceptualdistancebasedonfuzzyontology AT ramdanemaamri unexpectedrulesusingaconceptualdistancebasedonfuzzyontology AT yacinekissoum unexpectedrulesusingaconceptualdistancebasedonfuzzyontology AT maamarsedrati unexpectedrulesusingaconceptualdistancebasedonfuzzyontology |