Partially supervised clustering algorithms for pattern recognition

مقدم أطروحة جامعية

Labzour, Nadiyah Tazi

مشرف أطروحة جامعية

Bin Said, Amini

الجامعة

جامعة الأخوين

الكلية

كلية الهندسة و العلوم

القسم الأكاديمي

علوم الحاسب

دولة الجامعة

المغرب

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

1998

الملخص الإنجليزي

This thesis describes a large class of models called semi-supervised clustering.

Algorithms in this category are (i) clustering algorithms that (ii) use a finite design set of labeled data to {Hi) help clustering algorithms partition a finite set of unlabeled data and then (iv) terminate without the capability to label other points.

First, we show that the semi-supervised point-prototype clustering algorithm (ssPPC) as described in [5] can produce degenerate partitions of the unlabeled data set.

We propose two alternative approaches that guarantee non-degenerate classes.

We apply the improved algorithms to Iris data set and show that their performance is superior to the IDS decision tree and Quick propagation neural networks.

Then, we apply the general partially supervised clustering approach to agglomerative hierarchical clustering (AHC) algorithms used with relational data, we call this procedure semi-supervised agglomerative hierarchical clustering (ssAHC) algorithm, we show through experimentation that the partially-supervised clustering approach helps AHC algorithms improve their results.

Finally, we apply the semi-supervised clustering approach (ssAHC) to text categorization.

We apply ssAHC to Reuters database of documents, and show that its performance is superior to Bayes classifier, and Expectation Maximization algorithm combined with Bayes classifier, we show that the semi-supervised technique helps AHC get better performance.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الموضوعات

• الإدراك الحسي
• الإدراك
• الخوارزميات

عدد الصفحات

67

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-647589