يجب تسجيل الدخول أولا
Unexpected Rules Using A Conceptual Distance Based On Fuzzy Ontology
| المصدر: | مجلة جامعة الملك سعود - علوم الحاسب والمعلومات |
|---|---|
| الناشر: | جامعة الملك سعود |
| المؤلف الرئيسي: | Hamani, Mohamed Said (Author) |
| مؤلفين آخرين: | Maamri, Ramdane (Co-Author) , Kissoum, Yacine (Co-Author) , Sedrati, Maamar (Co-Author) |
| المجلد/العدد: | مج26, ع1 |
| محكمة: | نعم |
| الدولة: |
السعودية |
| التاريخ الميلادي: |
2014
|
| الصفحات: | 99 - 109 |
| DOI: |
10.33948/0584-026-001-010 |
| ISSN: |
1319-1578 |
| رقم MD: | 973034 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | science |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Fuzzy Ontology | Unexpectedness | Association Rule | Domain knowledge | Interestingness | Conceptual Distance
|
| رابط المحتوى: |
| المستخلص: |
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. |
|---|---|
| ISSN: |
1319-1578 |
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