Application of Self-Organizing Maps in Text Clustering: A Review <s> Text Clustering And Its Recent Research And Development <s> In a document retrieval, or other pattern matching environment where stored entities (documents) are compared with each other or with incoming patterns (search requests), it appears that the best indexing (property) space is one where each entity lies as far away from the others as possible; in these circumstances the value of an indexing system may be expressible as a function of the density of the object space; in particular, retrieval performance may correlate inversely with space density. An approach based on space density computations is used to choose an optimum indexing vocabulary for a collection of documents. Typical evaluation results are shown, demonstating the usefulness of the model. <s> BIB001 </s> Application of Self-Organizing Maps in Text Clustering: A Review <s> Text Clustering And Its Recent Research And Development <s> Keyphrases provide semantic metadata that summarize and characterize documents. This paper describes Kea, an algorithm for automatically extracting keyphrases from text. Kea identifies candidate keyphrases using lexical methods, calculates feature values for each candidate, and uses a machine-learning algorithm to predict which candidates are good keyphrases. The machine learning scheme first builds a prediction model using training documents with known keyphrases, and then uses the model to find keyphrases in new documents. We use a large test corpus to evaluate Kea's effectiveness in terms of how many author-assigned keyphrases are correctly identified. The system is simple, robust, and publicly available. <s> BIB002 </s> Application of Self-Organizing Maps in Text Clustering: A Review <s> Text Clustering And Its Recent Research And Development <s> A journal article is often accompanied by a list of keyphrases, composed of about five to fifteen important words and phrases that capture the article’s main topics. Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase extraction is to select keyphrases from within the text of a given document. Automatic keyphrase extraction makes it feasible to generate keyphrases for the huge number of documents that do not have manually assigned keyphrases. Good performance on this task has been obtained by approaching it as a supervised learning problem. An input document is treated as a set of candidate phrases that must be classified as either keyphrases or non-keyphrases. To classify a candidate phrase as a keyphrase, the most important features (attributes) appear to be the frequency and location of the candidate phrase in the document. Recent work has demonstrated that it is also useful to know the frequency of the candidate phrase as a manually assigned keyphrase for other documents in the same domain as the given document (e.g., the domain of computer science). Unfortunately, this keyphrase-frequency feature is domain-specific (the learning process must be repeated for each new domain) and training-intensive (good performance requires a relatively large number of training documents in the given domain, with manually assigned keyphrases). The aim of the work described here is to remove these limitations. In this paper, I introduce new features that are conceptually related to keyphrase-frequency and I present experiments that show that the new features result in improved keyphrase extraction, although they are neither domain-specific nor training-intensive. The new features are generated by issuing queries to a Web search engine, based on the candidate phrases in the input document. The feature values are calculated from the number of hits for the queries (the number of matching Web pages). In essence, these new features are derived by mining lexical knowledge from a very large collection of unlabeled data, consisting of approximately 350 million Web pages without manually assigned keyphrases. <s> BIB003 </s> Application of Self-Organizing Maps in Text Clustering: A Review <s> Text Clustering And Its Recent Research And Development <s> Document clustering is an aggregation of related documents to a cluster based on the similarity evaluation task between documents and the representatives of clusters. Terms and their discriminating features of terms are the clue to the clustering and the discriminating features are based on the term and document frequencies. Feature selection method on the basis of frequency statistics has a limitation to the enhancement of the clustering algorithm because it does not consider the contents of the cluster objects. In this paper, we adopt a content-based analytic approach to refine the similarity computation and propose a keyword-based clustering algorithm. Experimental results show that content-based keyword weighting outperforms frequency-based weighting method. <s> BIB004 </s> Application of Self-Organizing Maps in Text Clustering: A Review <s> Text Clustering And Its Recent Research And Development <s> Search results clustering is an attempt to automatically organise a linear list of document references returned by a search engine into a set of meaningful thematic categories. Such a clustered view helps the users to identify documents of interest more quickly. One search results clustering method is the description-comes-first approach, whereby using a dimensionality reduction technique a number of meaningful group labels are identified, which then determine the content of the actual clusters. The aim of this project was to compare how three different dimensionality reduction techniques would perform as parts of the description-comes-first method in terms of quality of clustering and computational efficiency. The evaluation stage was based on the standard merge-then-cluster model, in which we used the Open Directory Project web catalogue as a source of human-clustered document references. During the course of the project we implemented a number of dimensionality reduction techniques in Java and integrated them with our description-comes-first search results clustering algorithm. We also created a simple benchmarking application, which we used to gather data for further comparisons and analysis. Finally, we have chosen one dimensionality reduction technique that performed best both in terms of clustering quality and computational efficiency. <s> BIB005
Text clustering is an unsupervised process that is not dependent on the prior knowledge of data collection, and based solely on the similarity relationship between documents in the collection to separate the document collection into some clusters. The general mathematical description of text clustering can be depicted as follows: Suppose C = {d 1 , d 2 , … , d n } is a collection of documents to be clustered, each document d i can be represented as high-dimensional space vectord i = {w 1 , w 2 , … , w i } by the famous vector space model (VSM), where w i means the weight of d i on feature j. The purpose of text For hard clustering, each document can belong to only one class, i.e. C i ∩ C j = Φ. Whereas for soft clus-Developments and Applications of Self-Organizing Maps 206 tering, one document may belong to multiple clusters. Membership degree μ ij can be used to denote how much d i belongs to cluster C j . Compared with other data types, text data is semi-structured. This makes man databasebased algorithms does not apply to text clustering. One important preprocessing step for text clustering is to consider how the text content can be represented in the form of mathematical expression for further analysis and processing. The Common method is Salton's vector space model BIB001 (Vector Space Model, VSM). The basic idea is: one feature space are constructed firstly, each dimension means one term, which comes from the key words of each document. Then each document is represented as one vector in this feature space. The document vector is usually a sparse vector as the dimension is very huge. Dimensionality reduction is an essential step in text clustering. There are several techniques to reduce the dimension of the high-dimensional feature vector. PCA (Principal Component Analysis) method is one of the widely used dimension reduction techniques. Given an n × m-order document-term matrix, the k eigenvectors of the PCA with an m × m-order covariance matrix is used to reduce the dimension of the word space, and ultimately resulted in a k-term space dimension, which is much smaller than m. LSI (Latent Semantic the Indexing) method is also widely used in the field of information retrieval, dimensionality reduction. It is in essence similar with the PCA. LSI make singular value decomposition not on covariance matrix, but on the initial n × m-order documentterm matrix, and then selecting these singular eigenvectors as representative, thereby reduces the dimension. Another problem is how to extract important features from documents. Mark P. Sinka and David W. Corne BIB004 argue that stop word removal will improve the text clustering effect. They also pointed out that after obtaining all unique words in the collection, you can only keep some high-frequency words to construct the space. Anton V. Leouski and W. Bruce Crof demonstrated that for each document, it is necessary to select only some important words to represent the document, and can basically meet the needs of the cluster without impacting clustering results. Literature  proposed a method to extract the key words in the document as features Literature  use latent semantic indexing (LSI) method to compress the dimension of the clustering feature space. Besides, ZhengYu Niu BIB005 and STANI-SŁAW OSIŃSKI  , etc also performed research on feature selection. Assume there are five documents doc1 doc2, doc3, doc4, and doc5. For each document, the first steps are segmenting, stop word removal, and word frequency counting. In order to improve the clustering efficiency, only the words which frequency is above a certain threshold value are used to construct the feature space. Studies have shown that such a treatment will not have an adverse impact on the clustering quality. Then the feature space can be constructed by using the term set which comes from all these terms. Each document is represented as a vector in the feature space. Fig.2 . depicts the preprocessing steps for text clustering. Suppose the feature space is (apple, banana in the cat, window), and feature words frequency threshold is 2, then the following example document-term matrix can be formed: As all documents are represented as the vector in the same feature space, thus it is more convenient for computing the document similarity. In fact, the similarity calculation is very frequent for most clustering algorithms. In addition, as there are usually many common words in different documents, the actual dimension of the feature space is less than the sum of the number of words selected from each document. The evaluation of word importance. Take a science paper as an example, it is shown that about 65% to 90% author-marked keywords can be found in the main content in the original paper BIB002 . This means that by importance evaluation, the key words can be extracted from documents to represent the main content. Basically, keyword extraction can be seen as a supervised machine learning problems; this idea is first proposed by Turney BIB003 . Turney also make a comparative study based on genetic algorithms and decision treebased keywords extraction algorithm. Factors which can denote the word importance includes word frequency, word location (title, caption and etc.). Many researches showed that high-frequency words are the more important words. Some typical keyword extraction system has been listed in 