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Article
Comparison Between fuzzy C-Means Clustering (FCM) and geometrically guided condition Fuzzy C-Means clustering (ggc FCM)
مقارنة بين وسائل تجميع C-غامض (FCM) وحالة غامضة موجهة هندسيا المجموعات C-يعني (GGC FCM

Author: ZAKI .S. TOWFIK
Journal: Diyala Journal For Pure Science مجلة ديالى للعلوم الصرفة ISSN: 83732222 25189255 Year: 2010 Volume: 6 Issue: 2 Pages: 32-49
Publisher: Diyala University جامعة ديالى

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Abstract

This paper compare between the traditional fuzzy C-Means clustering FCM and aproposed technique approach to geometrically guided fuzzy clustering. A modified fuzzy CMeansclustering (FCM), is extended to incorporate a priori geometrical information from spatialdomain in order to improve image segmentation. This leads to a new algorithm where the clusterguidance is determined by the membership values on neighboring pixels. The algorithm of FCMis tested on synthetic and real image to demonstrate the improved image segmentation comparedto traditional FCM.

المقارنة بين تَجَمُّع التضبيب بواسطة الوسيط C مع التَجَمُّع التضبيب الهندسي الموجه المشرط بواسطة الوسيط C يتم في هذا البحث المقارنة بين تَجَمُّع التضبيب بواسطة الوسيط C (التقليدي) و مع نظرة تقنية مقترحة إلى التَجَمُّع التضبيب الهندسي الموجه المشرط بواسطة الوسيط C. أي ان التجمّعُ التضبيب بواسطة الوسيط C يمكن تطويره ليشمل عملية دمج معلوماتَ هندسيةَ أولية. ضمن المجالِ المكانيِ ليقوم بعملية تُحسّينَ تجزئية الصورةِ. هذا يُؤدّي إلى خوارزمية جديدة بدلا من خوارزمية تَجَمُّع التضبيب بواسطة الوسيط C (التقليدي) يعمل على التوجيه بتحديد المعلومات على شكل عنقودي بواسطة قيم العضويةَ لنقاطِ الشاشة المتجاورة. إنّ خوارزميةَ تَجَمُّع التضبيب الهندسي الموجه المشرط بواسطة الوسيط C تم اختيارهاُ على الصورةِ الصناعيةِ و الصورة الحقيقيةِ لعَرْض تجزئة الصورةِ المُحسَّنِة بالمقارنة مع تَجَمُّع التضبيب بواسطة الوسيط تقليدي.


Article
Basic Steps to Get Data QualitY for Data Mining
خطوات اساسية لتحسين نوعية البيانات لغرض تعدين البيانات

Author: ZAKI.S. TOWFIK
Journal: Journal of College of Education مجلة كلية التربية ISSN: 18120380 Year: 2010 Issue: 6 Pages: 117-128
Publisher: Al-Mustansyriah University الجامعة المستنصرية

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Abstract

The Data extracted from many sources will be integrated andthen transform into suitable form. Tthese data may be includes manyerrors and noise or inconsistencies data. It is necessary to clean thedata to get quality data before the data mined from errors and noisedata. The cleaning is the first task before any data analysis' Theresultant of cleaning analysis/model can be stamped for data qualitywhich very impotent for data minig process because without dataquality the algorithms of data nining can not work well or the resultof algorithms not good.Therefor this paper deal with Basics steps to clean data thatextracted from many sources to get good quality data for data miningalso reduce processing time, storage data and reducing costs andincreasing profits, for this case an implementation for data selectedfrom clinical chemical test for yarmook hospital education to detectand remove the errors or noise and or inconsistencies data'


Article
Proposed Method for Optimizing Fuzzy linear programming Problems by using Two-Phase Technique

Authors: Sabiha Fathil Jawad --- Zaki .S. Towfik
Journal: Iraqi Journal for Electrical And Electronic Engineering المجلة العراقية للهندسة الكهربائية والالكترونية ISSN: 18145892 Year: 2010 Volume: 6 Issue: 2 Pages: 89-96
Publisher: Basrah University جامعة البصرة

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Abstract

Fuzzy linear programming (FLP )is an application of fuzzy set theoryin linear decision making problemsand most of these problems arerelated to linear programmingcontains fuzzy constrains or crispobjectives function or contains crispconstrains with fuzzy objectivesfunction, which called fuzzy linearprogramming (FLP) with tripletfuzzy numbers consist a hybridfuzzy. The crisp constrains used inthe problems of types (= or ≥) with aproposed optimization fuzzyobjectives and fuzzy constrains. Inthis paper proposed method forsolving fuzzy linear programmingproblem by using Two-phasetechnique to solve the problem andto determine the optima crispobjectives.


Article
Application of Decision Tree as a Data Mining Tool in a health care

Authors: Sabiha Fathil Jawad صبحة فتحي جواد --- ZAKI .S. TOWFIK زكي سعيد توفيق
Journal: Journal of Kufa for Mathematics and Computer مجلة الكوفة للرياضيات والحاسوب ISSN: 11712076 Year: 2012 Volume: 1 Issue: 5 Pages: 102-109
Publisher: University of Kufa جامعة الكوفة

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Abstract

Abstract: This work demonstrates the application of decision tree, as data mining tool, in the health care system. Data mining has the capability for classification, prediction, estimation, and pattern recognition by using health databases. Databases of health systems contain significant information for decision making. It could be properly revealed with the application of appropriate data mining techniques. Decision trees are employed for identifying valuable information in health databases. In this paper Decision tree as a data mining tools is used for predication the spread types of disease of hepatitis virus in reigns that high affect to people with different temperature and prevention of this disease by using rules that needed to predicate the diseases .

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