A data mining approach for categorising web documents

مقدم أطروحة جامعية

Qaddum, Kifayah Said

مشرف أطروحة جامعية

Yusuf, Nadiyah I.
Thabtah, Fadi

الجامعة

جامعة فيلادلفيا

الكلية

كلية تكنولوجيا المعلومات

القسم الأكاديمي

قسم علم الحاسوب

دولة الجامعة

الأردن

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2008

الملخص الإنجليزي

-The core step of KDD is Data Mining.

Data Mining applies efficient algorithms to extract interesting patterns and regularities from the data.

As volume of information in digital form increases, the use of Text Categorization techniques, which aim at finding relevant information, becomes more necessary.

To improve the quality of the classification process form textual data sets, Associative Classification, which utilizes the association rule discovery techniques to construct classification systems, is evaluated in this thesis.

Particularly, we developed an associative classification vertical mining algorithm representation in order to improve the accuracy of the classification phase, and to reduce the size of the memory required to store intermediate TIDS in the mining process.

Considering the fact that vertical data structure supports fast frequency counting via intersection operations on transaction identifiers (TIDS), this should improve accuracy and decrease memory usage.

This thesis demonstrates the problem of using Associative Classification to solve Text Categorization problem, and utilize Diffset structure as a mining approach.

التخصصات الرئيسية

الرياضيات

الموضوعات

• الإجراءات الأمنية
• التنقيب في البيانات

عدد الصفحات

88

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-546174