A Quantum Swarm Evolutionary Algorithm For Mining Association Rules In Large Databases
| المصدر: | مجلة جامعة الملك سعود - علوم الحاسب والمعلومات |
|---|---|
| الناشر: | جامعة الملك سعود |
| المؤلف الرئيسي: | Ykhlef, Mourad (Author) |
| المجلد/العدد: | مج23, ع1 |
| محكمة: | نعم |
| الدولة: |
السعودية |
| التاريخ الميلادي: |
2011
|
| الصفحات: | 1 - 6 |
| DOI: |
10.33948/0584-023-001-001 |
| ISSN: |
1319-1578 |
| رقم MD: | 971934 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | science |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Quantum Evolutionary Algorithm | Swarm Intelligence | Association Rule Mining | Fitness
|
| رابط المحتوى: |
| المستخلص: |
Association rule mining aims to extract the correlation or causal structure existing between a set of frequent items or attributes in a database. These associations are represented by mean of rules. Association rule mining methods provide a robust but non-linear approach to find associations. The search for association rules is an NP -complete problem. The complexities mainly arise in exploiting huge number of database transactions and items. In this article we propose a new algorithm to extract the best rules in a reasonable time of execution but without assuring always the optimal solutions. The new derived algorithm is based on Quantum Swarm Evolutionary approach; it gives better results compared to genetic algorithms. |
|---|---|
| ISSN: |
1319-1578 |
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