نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشگاه تربیت دبیر شهید رجایی، دانشجوی کارشناسی ارشد
2 دانشگاه تربیت دبیر شهید رجائی، دکتری مهندسی کامپیوتر
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
Clustering is one of the most important field of data mining that aims to divide data into meaningful
subsets which are called clusters. This technique involves the process of finding natural groupings in the
data set based on the similarities and di similarities which a little or no information about data are
available. Over the decades, many clustering algorithms are created in different approaches or a
combination of them. In this paper, an algorithm based on density and hierarchical approaches is
presented. DBSCAN is one of the algorithms presented in the density-based approach. This algorithm
requires two parameters that its determination is a great challenge. In the proposed method, DBSCAN
algorithm parameters can be set without user involvement, so that potential clusters are found
automatically. The clusters which are so close to each other are merged together until the quality of the
final clusters to be enhanced properly. Thus, clusters could be more accurate and high quality. Finally, in
order to test the new proposed algorithm, the real dataset in the UCI machine learning repository was
used. The results indicate that the new algorithm is more efficient and accurate, and its speed is better
than previous methods.
کلیدواژهها [English]
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