MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters

• 主要な著者: Wan Maseri, Wan Mohd, Beg, Abul Hashem, Herawan, Tutut, Fazley Rabbi, Khandakar
• フォーマット: 論文
• 言語: English
• 出版事項: Springer, Berlin, Heidelberg 2012
• 主題: QA76 Computer software
• オンライン･アクセス: http://umpir.ump.edu.my/id/eprint/27004/1/MaxD%20K-Means-%20A%20clustering%20algorithm%20for%20auto-generation%20of%20centroids.pdf; http://umpir.ump.edu.my/id/eprint/27004/; https://doi.org/10.1007/978-3-642-34289-9_22; https://doi.org/10.1007/978-3-642-34289-9_22

K-Means is one of the unsupervised learning and partitioning clustering algorithms. It is very popular and widely used for its simplicity and fastness. The main drawback of this algorithm is that user should specify the number of cluster in advance. As an iterative clustering strategy, K-Means algor...