期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2014
卷号：4
期号：2
页码：114-117
出版社：International Journal of Soft Computing & Engineering
摘要：K-Means and Kohonen SOM clustering are two major analytical tools for unsupervised forest datasets. However, both have their innate disadvantages. Clustering is currently one of the most crucial techniques for dealing with massive amount of heterogeneous information on the databases, which is beyond human being’s capacity to digest. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. Also, as clusters grow in size, the actual expression patterns become less relevant. K-means clustering requires a specified number of clusters in advance and chooses initial centroids randomly; in addition, it is sensitive to outliers. SOM We present an improved approach to combined merits of the two and discard disadvantages.
