Application of Self-Organizing Maps in Text Clustering: A Review <s> End. <s> This paper presents the results of an experimental study of some common document clustering techniques. In particular, we compare the two main approaches to document clustering, agglomerative hierarchical clustering and K-means. (For K-means we used a “standard” K-means algorithm and a variant of K-means, “bisecting” K-means.) Hierarchical clustering is often portrayed as the better quality clustering approach, but is limited because of its quadratic time complexity. In contrast, K-means and its variants have a time complexity which is linear in the number of documents, but are thought to produce inferior clusters. Sometimes K-means and agglomerative hierarchical approaches are combined so as to “get the best of both worlds.” However, our results indicate that the bisecting K-means technique is better than the standard K-means approach and as good or better than the hierarchical approaches that we tested for a variety of cluster evaluation metrics. We propose an explanation for these results that is based on an analysis of the specifics of the clustering algorithms and the nature of document <s> BIB001 </s> Application of Self-Organizing Maps in Text Clustering: A Review <s> End. <s> Document clustering has not been well received as an information retrieval tool. Objections to its use fall into two main categories: first, that clustering is too slow for large corpora (with running time often quadratic in the number of documents); and second, that clustering does not appreciably improve retrieval. We argue that these problems arise only when clustering is used in an attempt to improve conventional search techniques. However, looking at clustering as an information access tool in its own right obviates these objections, and provides a powerful new access paradigm. We present a document browsing technique that employs document clustering as its primary operation. We also present fast (linear time) clustering algorithms which support this interactive browsing paradigm. <s> BIB002
Researchers often use two different methods to cut the hierarchical relationships. One is to use the number of clusters as segmentation standard; another method is using the similarity as the segmentation standard, that is, when the similarity between two clusters is lower than a given threshold, the clustering algorithm will stop. Besides, it has been shown that the clustering entropy  can be used as the termination conditions of the hierarchical clustering method: The first expression in the right side of the formula is the intra-cluster entropy; the second means the inter-cluster entropy. When En is smallest, the clustering result achieves optimum value. c 0 is the center of all the samples. p i ( j) is the i documents for cluster j. p 0 ( j) is the center of the jth clusters. K is the number of clusters, n j is the number of documents in cluster j. K-means clustering algorithm is the typical dynamic partition method BIB001 [38] [39] BIB002 . The basic steps [41] are as follows: 1. Randomly select K documents, which represent initial cluster centroids.