Application of Self-Organizing Maps in Text Clustering: A Review <s> Introduction <s> Abstract We introduce information retrieval strategies which are based on automatic hierarchic clustering of documents. We discuss the evaluation of retrieval strategies and show, using a subset of the Cranfield Aeronautics document collection, that cluster-based retrieval strategies can be devised which are as effective as linear associative retrieval strategies and much more efficient. Finally, we outline how cluster-based retrieval may be extended to large growing document collections and indicate some ways in which the effectiveness of cluster-based retrieval strategies may be improved. <s> BIB001 </s> Application of Self-Organizing Maps in Text Clustering: A Review <s> Introduction <s> The processing time and disk space requirements of an inverted index and top-down cluster search are compared. The cluster search is shown to use both more time and more disk space, mostly due to the large number of cluster centroids needed by the search. When shorter centroids are used, the efficiency of the cluster search improves, but the inverted index search remains more efficient. <s> BIB002 </s> Application of Self-Organizing Maps in Text Clustering: A Review <s> Introduction <s> We discuss the merits of building text categorization systems by using supervised clustering techniques. Traditional approaches for document classification on a predefined set of classes are often unable to provide sufficient accuracy because of the difficulty of fitting a manually categorized collection of documents in a given classification model. This is especially the case for heterogeneous collections of Web documents which have varying styles, vocabulary, and authorship. Hence, we investigate the use of clustering in order to create the set of categories and its use for classification of documents. Completely unsupervised clustering has the disadvantage that it has difficulty in isolating sufficiently fine-grained classes of documents relating to a coherent subject matter. We use the information from a preexisting taxonomy in order to supervise the creation of a set of related clusters, though with some freedom in defining and creating the classes. We show that the advantage of using partially supervised clustering is that it is possible to have some control over the range of subjects that one would like the categorization system to address, but with a precise mathematical definition of how each category is defined. An extremely effective way then to categorize documents is to use this a priori knowledge of the definition of each category. We also discuss a new technique to help the classifier distinguish better among closely related clusters. <s> BIB003
Text clustering is one of the most important text mining research directions. Despite the loss of some details, clustering technology simplifies the structure of data set, so that people can observe the data from a macro point of view. After clustering process, the text data set can be divided into some different clusters, making the distance between the individuals in the same cluster as small as possible, while the distance between the different categories as far away from each other as possible. Similar as text classification, text clustering is also the technology of processing a large number of texts and gives their partition.What is different is that text clustering analysis of the text collection gives an optimal division of the category without the need for labeling the category of some documents by hand in advance, so it is an unsupervised machine learning method. By comparison, text clustering technology has strong flexibility and automatic processing capabilities, and has become an important means of effective organization and navigation of text information. Jardine and van Rijsbergen made the famous clustering hypothesis: closely associated documents belong to same category and the same request BIB001 . Text clustering can also act as the basic research for many other applications. It is a preprocessing step for some natural language processing applications, e.g., automatic summarization, user preference mining, or be used to improve text classification results. YC Fang, S. Parthasarathy,  and Charu BIB003 use clustering techniques to cluster users' frequent query and then the results to update the FAQ of search engine sites. Although both text clustering and text classification are based on the idea of class, there are still some apparent differences: the classification is based on the taxonomy, the category distribution has been known beforehand. While the purpose of text clustering is to find the top-ic structure of documents  [5]   [8] BIB002  . Yasemin Kural  made a lot of experiments and compared the clustering mode and linear array mode for search engine, the results show that the former can indeed increase information access efficiency greatly. Although there are many clustering methods, SOM has attracted many researchers in recent years. In this chapter, we reviewed the application of Self-Organizing Maps in Text Clustering. Our recent works on SOM based text clustering are also introduced briefly. The remaining of this chapter is organized as follows. Section 2 gives a review about the advances in text clustering and SOM; section 3 presents our recent work on application of self-organizing maps in text clustering. Then in section 4 some conclusions and discussions are given.