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Register a new userEvaluation of Estimating Number of Clusters Methods in Case of Agglomerative Hierarchical Clustering
تقييم طرق تقدير عدد العناقيد في حالة العنقدة الهرمية التكتلية
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إن الهدفَ الرئيسي من عمليةِ التنقيب في البيانات هو استخراج المعلومات و اكتشاف المعرفةِ من قواعدِ البياناتِ الضخمة، حيث تُعتبر العنقدة أحد أهم الوظائف التي يمكن القيامَ بها في هذا المجال. يوجدُ العديدُ من طرقِ و خوارزمياتِ العنقدة، إلا أن تحديد أو تقدير عدد العناقيد التي يجبُ استخراجها من عينةٍ ما يعتبر من أهم القضايا التي تواجها معظمُ هذه الطرق. يركز هذا البحث على مسألةِ تقديرِ عدد العناقيد في حالةِ العنقدة الهرمية. نقَدم في هذا البحث تقييماً لثلاثةٍ من أكثرِ الطرقَ شيوعاً في تقديرِ عددِ العناقيد.
The main goal of data mining process is to extract information and discover knowledge from huge databases, where the clustering is one of the most important functionalities which can be done in this area. There are many of clustering algorithms and methods, but determining or estimating the number of clusters which should be extracted from a dataset is one of the most important issues most of these methods encounter it. This research focuses on the problem of estimating number of clusters in the case of agglomerative hierarchical clustering. We present an evaluation of three of the most common methods used in estimating number of clusters.
References used
Amorim R, Hennig C, 2015 Recovering the number of clusters in data sets with noise features using feature rescaling factors, Information Sciences, vol. 324. 126-145
Arbelaitz O, Gurrutxaga I, Muguerza J, Perez J, Perona I. 2013 An extensive comparative study of cluster validity indices, Pattern Recognition, Vol. 46. 243-256
Berry Michael J.A, Linoff Gordon S, 2004- Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Wiley, 2nd edition USA, 672p
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