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Register a new userEmploying Data Mining Algorithms in Traffic Accidents Analyzing
توظيف خوارزميات التنقيب في البيانات لتحليل حوادث المرور
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تقدم هذه الورقة البحثية مقارنة لمجموعة من خوارزميات التنقيب في البيانات Data Mining Algorithms فيما يتعلق بتحليل حوادث المرور، انطلاقاً من مرحلة إدخال البيانات، و ذلك من خلال تحليل بنية التقارير الإحصائية الموجودة في فرع مرور اللاذقية وصولاً إلى مرحلة التنقيب في البيانات التي تستطيع إيجاد آلية قادرة على دراسة العوامل التي تلعب دوراً في حادث المرور بذكاء من أجل الربط و تحديد مدى العلاقة بينها و أهميتها في تسبب الحادث المروري، و ذلك بعد تصميم بنية مستودع البيانات على أساس قاعدة البيانات التي تم بناؤها لتخزين المعلومات، تم في هذا البحث ذكر مجموعة من النماذج التي تم اختبارها و التي تشكل عينة عن الاختبارات التي بنيت عليها نتائج البحث.
In this paper we introduce a comparison for some of data mining algorithm for traffic accidents analysis. We start by describing available data for entry by analyzing the structure of statistical reports in Lattakia traffic directorate, and proceed to data mining stage which enables us to smart study of factors that play roles in traffic accident and find its inter-relations and importance for causing traffic accident. That comes after building data warehouse upon the database we built to store the data we gathered. In this research we list a some of models was tested which is a sample of a many cases we checked to have the research results.
References used
(SUKHAI ,ANESH., P JONES,ANDY., HAYNES ,ROBIN., “Epidemiology And Risk Of Road Traffic Mortality In South Africa.” South African Geographical Journal 91 (1) 4 – 15 (2009
(World Health Organaizaton, www.who.org, “World report on road traffic injury prevention summary” (2004
(Alex, A.Freitas., “A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery.” Postgraduate Program in Computer Science,Pontificia Universidade Catolica do Parana Rua Imaculada Cnceicao,1155 (2011
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The objective of this paper is presenting an educational data
mining case study, by applying data mining techniques.
Data Mining
التنقيب في البيانات
التنقيب في بيانات المؤسسات التعليمية
برنامج ذكاء الأعمال الخاص بشركة مايكروسوفت
خوارزمية مايكروسوفت لأشجار القرار
خوارزمية مايكروسوفت للتجميع و الكشف عن الحالات الشاذة
خوارزمية مايكروسوفت لقواعد الارتباط
Educational Data Mining
SQL Server Business Intelligence Development Studio
Microsoft Decision Trees
Microsoft Clustering
Outlier Detection
Microsoft Association Rules
المزيد..
In this research, we offered a new and simple way of
Handwriting Characters Recognition. This way extracts positions of
the black points from binary images (black, white) according to
certain coordinates which are used in the stages of training an
d
testing. The extracted positions are stored in a database according
to appropriate structure for predictive data mining.
We used training data to build a predictive model which helps
in Recognition testing data depending on the data stored in the
database. We have conducted a number of tests on different
samples of handwriting character images. We got accurate results,
within the required conditions.
This study aims to measure and analyse the social and economical
cost of Traffic Accidents in the Hashimyat Jordanian Kingdom
governorates.
The Study employs the descriptive approach to achieve its goals.
Data information were collected from thei
r origins and processed
employing the techniques: partial correlation, multiple regression, and
sum of ranke analysis.
The study concluded the existence of differences in the social and
economic costs of traffic accidents between the Kingdom governorates
and this difference is related differences in population figures, number of
registed vehicles, and traffic accident counts.
The study, classified the kingdom governorates according to the
levels of socio economic costs into three levels: High cost level, medium
and low.
The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles and animals. Many techniques have been propos
ed for processing, managing and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a roadmap from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.
Data mining is becoming a pervasive technology in activities as diverse as using historical data to predict the success of a marketing campaign looking for patterns in financial transactions to discover illegal activities. From this perspective it wa
s just a matter of time for the discipline to reach the important area of computer security This research presents a collection of research efforts on the use of data mining in computer security.
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