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Comparative Analysis of K-Means and Bisecting K-Means Algorithms for Brain Tumor Detection

机译:K均值和平分K均值算法用于脑肿瘤检测的比较分析

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摘要

Brain is the most precious part of the human body. Therefore, it is entirely necessary to substantially distinguish the different regions of the brain for diagnosing any anomalies in medical science. Most recently, data mining provides some clustering algorithms for efficiently detecting the diverse area of the brain. In this paper, different clustering algorithms for division display have been studied. The essential thought of clustering is to assign the similarity between the distance, which refers to the data to measure the similarity of the size of the data is ordered until all the data gathering is finished. But the primary point is to demonstrate the examination of the different clustering algorithms to discover which algorithm will be most reasonable for the users.
机译:大脑是人体最宝贵的部分。因此,完全有必要区分大脑的不同区域以诊断医学中的任何异常情况。最近,数据挖掘提供了一些聚类算法,可以有效地检测大脑的各个区域。本文研究了用于分割显示的不同聚类算法。聚类的基本思想是在距离之间分配相似度,这是指要测量数据相似性的数据的大小,有序直到所有数据收集完成。但是重点是要演示对不同聚类算法的检查,以发现哪种算法最适合用户。

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