مع التطور الهائل في جميع المجالات العلمية و الاقتصادية و السياسية و غيرها ظهرت الحاجة لإيجاد طرق غير تقليدية للتعامل من خلالها مع البيانات بجميع أنماطها ( النصية و المرئية و الصوتية و غيرها ) والتي أصبحت ذات حجوم كبيرة جداً في هذه الأيام. فكان لابد من إيجاد طرق جديدة لاستنباط المعرفة و المعلومات المخبأة ضمن هذا الكم الهائل من
البيانات كالاستعلام عن الزبائن الذين لديهم عادات شرائية متماثلة أو التوقعات المحتلمة لبيع سمعة معينة في إحدى المناطق الجغرافية و غيرها من الاستعلامات الاستنتاجية و التي تعتمد على تقنية التنقيب في البيانات. و تتم عملية التنقيب بعدة أساليب من أهمها أسلوب العنقدة (التجميع) Clustering و الذي يتم بعدة خوارزميات.
سوف نرّكز في بحثنا هذا على استخدام طريقة مدروسة لإيجاد المراكز الابتدائية لخوارزمية K-Medoids التي تقوم على مبدأ تقسيم البيانات إلى عناقيد كل عنقود يحوي بيانات متماثمة يَسهل التعامل معها بدلاً من اختيارها بالشكل العشوائي الذي يؤدي بدوره لظهور نتائج مختلفة وبطئ في تنفيذ الخوارزمية .
With the tremendous development in all areas of scientific,
economic, political and other appeared the need to find nontraditional ways in which to deal with all the data patterns (text, video and audio, etc.), which are becoming very large volumes these days. Was necessary to find new ways to develop knowledge and information hidden within this huge amount of data such as query for customers who have habits of purchasing the same or prospects for the sale of a particular commodity in one of the geographical areas and other queries deductive and based on the technology of data mining. The process of exploration in several of the most important methods of clustering method (assembly) Clustering, which are several algorithms.
We will focus in this research on the use of a way calculated to create centers of First Instance of the algorithm K-Medoids which is based on the principle of the division of data into clusters each
cluster contains a replica database easy to handle, rather than
selected as random which in turn leads to the emergence of different results and slow in the implementation of the algorithm.
References used
Dunham, M. H. 2003-Data Mining: Introductory and Advanced Topics. Prentice Hal Bazsalica, 328p
Kaufman,L. Rousseeuw,P2010-Finding Groups in Data: an .Introduction to Cluster Analysis. John,170p
Berry,J. Linoff.G2004-Data Mining Techniques For Marketing, Sales, and Customer Relationship Management, Indianapolis,150p
Ng,R, Han.J-2008-Efficient and Effective Clustering Methods for Spatial Data Mining, Conf, 144p
Xu Yifeng Chen Chunming. 2009 ONTOLOGY-BASED WEB MINING Computer Applications and software, 180p
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