Multilingual news clustering: Feature translation vs. identification of cognate named entities

In this paper we evaluate the influence of different document representations in the results of multilingual news clustering. We aim at proving whether or not the use of only named entities is a good source of knowledge for multilingual news clustering. We compare two approaches: one based on feature translation, and another based on cognate identification. Our main contribution is using only some categories of cognate named entities like document representation features to perform multilingual news clustering, without the need of translation resources. The results show that the use of cognate named entities, as the only type of features to represent news, leads to good multilingual clustering performance, comparable to the one obtained by using the feature translation approach.     

A collaborative filtering-based approach to personalized document clustering

Document clustering is an intentional act that reflects individual preferences with regard to the semantic coherency and relevant categorization of documents. Hence, effective document clustering must consider individual preferences and needs to support personalization in document categorization. Most existing document-clustering techniques, generally anchoring in pure content-based analysis, generate a single set of clusters for all individuals without tailoring to individuals' preferences and thus are unable to support personalization. The partial-clustering-based personalized document-clustering approach, incorporating a target individual's partial clustering into the document-clustering process, has been proposed to facilitate personalized document clustering. However, given a collection of documents to be clustered, the individual might have categorized only a small subset of the collection into his or her personal folders. In this case, the small partial clustering would degrade the effectiveness of the existing personalized document-clustering approach for this particular individual. In response, we extend this approach and propose the collaborative-filtering-based personalized document-clustering (CFC) technique that expands the size of an individual's partial clustering by considering those of other users with similar categorization preferences. Our empirical evaluation results suggest that when given a small-sized partial clustering established by an individual, the proposed CFC technique generally achieves better clustering effectiveness for the individual than does the partial-clustering-based personalized document-clustering technique.     

Document clustering method using dimension reduction and support vector clustering to overcome sparseness

Many studies on developing technologies have been published as articles, papers, or patents. We use and analyze these documents to find scientific and technological trends. In this paper, we consider document clustering as a method of document data analysis. In general, we have trouble analyzing documents directly because document data are not suitable for statistical and machine learning methods of analysis. Therefore, we have to transform document data into structured data for analytical purposes. For this process, we use text mining techniques. The structured data are very sparse, and hence, it is difficult to analyze them. This study proposes a new method to overcome the sparsity problem of document clustering. We build a combined clustering method using dimension reduction and K-means clustering based on support vector clustering and Silhouette measure. In particular, we attempt to overcome the sparseness in patent document clustering. To verify the efficacy of our work, we first conduct an experiment using news data from the machine learning repository of the University of California at Irvine. Second, using patent documents retrieved from the United States Patent and Trademark Office, we carry out patent clustering for technology forecasting.     

Query-Sensitive Similarity Measures for Information Retrieval

The application of document clustering to information retrieval has been motivated by the potential effectiveness gains postulated by the cluster hypothesis. The hypothesis states that relevant documents tend to be highly similar to each other and therefore tend to appear in the same clusters. In this paper we propose an axiomatic view of the hypothesis by suggesting that documents relevant to the same query (co-relevant documents) display an inherent similarity to each other that is dictated by the query itself. Because of this inherent similarity, the cluster hypothesis should be valid for any document collection. Our research describes an attempt to devise means by which this similarity can be detected. We propose the use of query-sensitive similarity measures that bias interdocument relationships toward pairs of documents that jointly possess attributes expressed in a query. We experimentally tested three query-sensitive measures against conventional ones that do not take the query into account, and we also examined the comparative effectiveness of the three query-sensitive measures. We calculated interdocument relationships for varying numbers of top-ranked documents for six document collections. Our results show a consistent and significant increase in the number of relevant documents that become nearest neighbors of any given relevant document when query-sensitive measures are used. These results suggest that the effectiveness of a cluster-based information retrieval system has the potential to increase through the use of query-sensitive similarity measures.     

基于并行信息瓶颈的多语种文本聚类算法*

聚类算法在抽取文本数据中的模式结构时,忽略多个语种信息之间潜在的互补作用,得到的模式结构不能充分反映数据的内在信息.针对此问题,文中提出基于并行信息瓶颈的多语种文本聚类算法.首先使用词袋模型为文本数据的不同语种信息构建相应的相关变量.然后将多种相关变量引入并行信息瓶颈方法,通过最大化地保存模式结构与多个相关变量之间的信息,使得到的模式结构能够反映数据的多个语种信息.最后提出基于信息论的抽取合并方法优化文中算法的目标函数,保证其收敛到局部最优解.实验表明,文中算法能有效处理文本数据的多个语种信息,性能优于单语种聚类算法和现有的两类能够处理文本多语种信息的聚类算法.     

KnowledgeScope: managing knowledge in context

Knowledge repositories have been implemented in many organizations, but they often suffer from non-use. This research considers two key design factors that cause non-use: the extra burden on users to document knowledge in the repository, and the lack of a standard knowledge structure that facilitates knowledge sharing among users with different perspectives. We propose a design of a knowledge management system called KnowledgeScope that addresses these problems through (1) an integrated workflow support capability that captures and retrieves knowledge as an organizational process proceeds, i.e., within the context in which it is created and used, and (2) a process meta-model that organizes that knowledge and context in a knowledge repository. In this paper, we describe this design and report the results from implementing it in a real-life organization.     

Anomaly detection in web documents using crisp and fuzzy-based cosine clustering methodology

Cluster analysis is a primary tool for detecting anomalous behavior in real-world data such as web documents, medical records of patients or other personal data. Most existing methods for document clustering are based on the classical vector-space model, which represents each document by a fixed-size vector of weighted key terms often referred to as key phrases. Since vector representations of documents are frequently very sparse, inverted files are used to prevent a tremendous computational overload which may be caused in large and diverse document collections such as pages downloaded from the World Wide Web. In order to reduce computation costs and space complexity, many popular methods for clustering web documents, including those using inverted files, usually assume a relatively small prefixed number of clusters.We propose several new crisp and fuzzy approaches based on the cosine similarity principle for clustering documents that are represented by variable-size vectors of key phrases, without limiting the final number of clusters. Each entry in a vector consists of two fields. The first field refers to a key phrase in the document and the second denotes an importance weight associated with this key phrase within the particular document. Removing the restriction on the total number of clusters, may moderately increase computing costs but on the other hand improves the method’s performance in classifying incoming vectors as normal or abnormal, based on their similarity to the existing clusters. All the procedures represented in this work are characterized by two features: (a) the number of clusters is not restricted by some relatively prefixed small number, i.e., an arbitrary new incoming vector which is not similar to any of the existing cluster centers necessarily starts a new cluster and (b) a vector with multiple appearance n in the training set is counted as n distinct vectors rather than a single vector. These features are the main reasons for the high quality performance of the proposed algorithms. We later describe them in detail and show their implementation in a real-world application from the area of web activity monitoring, in particular, by detecting anomalous documents downloaded from the internet by users with abnormal information interests.     

A Multilingual Text Mining Approach Based on Self-Organizing Maps

This paper describes our work on developing a language-independent technique for discovery of implicit knowledge from multilingual information sources. Text mining has been gaining popularity in the knowledge discovery field, particularity with the increasing availability of digital documents in various languages from all around the world. However, currently most text mining tools mainly focus only on processing monolingual documents (particularly English documents): little attention has been paid to apply the techniques to handle the documents in Asian languages, and further extend the mining algorithms to support the aspects of multilingual information sources. In this work, we attempt to develop a language-neutral method to tackle the linguistics difficulties in the text mining process. Using a variation of automatic clustering techniques, which apply a neural net approach, namely the Self-Organizing Maps (SOM), we have conducted several experiments to uncover associated documents based on a Chinese corpus, Chinese-English bilingual parallel corpora, and a hybrid Chinese-English corpus. The experiments show some interesting results and a couple of potential paths for future work in the field of multilingual information discovery. Besides, this work is expected to act as a starting point for exploring the impacts on linguistics issues with the machine-learning approach to mining sensible linguistics elements from multilingual text collections.     

A spectral analysis approach to document summarization: Clustering and ranking sentences simultaneously

Automatic document summarization aims to create a compressed summary that preserves the main content of the original documents. It is a well-recognized fact that a document set often covers a number of topic themes with each theme represented by a cluster of highly related sentences. More important, topic themes are not equally important. The sentences in an important theme cluster are generally deemed more salient than the sentences in a trivial theme cluster. Existing clustering-based summarization approaches integrate clustering and ranking in sequence, which unavoidably ignore the interaction between them. In this paper, we propose a novel approach developed based on the spectral analysis to simultaneously clustering and ranking of sentences. Experimental results on the DUC generic summarization datasets demonstrate the improvement of the proposed approach over the other existing clustering-based approaches.     

基于矩阵谱分析的文本聚类集成算法

聚类集成技术可有效提高单聚类算法的精度和稳定性,其中的关键问题是如何根据不同的聚类成员组合为更好的聚类结果.文中引入谱聚类算法解决文本聚类集成问题,设计基于正则化拉普拉斯矩阵的谱算法(NLM-SA).该算法基于代数变换,通过求解小规模矩阵的特征值和特征向量间接获得正则化拉普拉斯矩阵的特征向量,并用于后续聚类.进一步研究谱聚类算法的关键思想,设计基于超边转移概率矩阵的谱算法(HTMSA).该算法通过求解超边的低维嵌入间接获得文本的低维嵌入,并用于后续K均值算法.在TREC和Reuters文本集上的实验结果验证NLMSA和HTMSA的有效性,它们都获得比其它基于图划分的集成算法更为优越的结果.HTMSA获得的结果比NLMSA略差,而时间和空间需求则比NLMSA低得多.     

Document clustering using locality preserving indexing and support vector machines

A method of document clustering based on locality preserving indexing (LPI) and support vector machines (SVM) is presented. The document space is generally of high dimensionality, and clustering in such a high-dimensional space is often infeasible due to the curse of dimensionality. In this paper, by using LPI, the documents are projected into a lower-dimension semantic space in which the documents related to the same semantic are close to each other. Then, by using SVM, the vectors in semantic space are mapped by means of a Gaussian kernel to a high-dimensional feature space in which the minimal enclosing sphere is searched. The sphere, when mapped back to semantics space, can separate into several independent components by the support vectors, each enclosing a separate cluster of documents. By combining the LPI and SVM, not only higher clustering accuracies in a more unsupervised effective way, but also better generalization properties can be obtained. Extensive demonstrations are performed on the Reuters-21578 and TDT2 data sets. This work was supported by National Science Foundation of China under Grant 60471055, Specialized Research Fund for the Doctoral Program of Higher Education under Grant 20040614017.     

A multilingual knowledge management system: A case study of FAO and WAICENT

This paper provides a case study of a multilingual knowledge management system for a large organization. In so doing we elicit what it means for a system to be “multilingual” and how that changes some previous research on knowledge management. Some researchers have viewed multilingual as meaning a multilingual user interface. However, that is only a small part of the story. In this case we find multilingual also refers to a broad range of “multilingual,” including multilingual knowledge resources, multilingual feedback from users, multilingual search, multilingual ontologies and other concerns.     

解决文本聚类集成问题的两个谱算法

聚类集成中的关键问题是如何根据不同的聚类器组合为最终的更好的聚类结果. 本文引入谱聚类思想解决文本聚类集成问题, 然而谱聚类算法需要计算大规模矩阵的特征值分解问题来获得文本的低维嵌入, 并用于后续聚类. 本文首先提出了一个集成算法, 该算法使用代数变换将大规模矩阵的特征值分解问题转化为等价的奇异值分解问题, 并继续转化为规模更小的特征值分解问题; 然后进一步研究了谱聚类算法的特性, 提出了另一个集成算法, 该算法通过求解超边的低维嵌入, 间接得到文本的低维嵌入. 在TREC和Reuters文本数据集上的实验结果表明, 本文提出的两个谱聚类算法比其他基于图划分的集成算法鲁棒, 是解决文本聚类集成问题行之有效的方法.     

A weighted common structure based clustering technique for XML documents

XML has recently become very popular as a means of representing semistructured data and as a standard for data exchange over the Web, because of its varied applicability in numerous applications. Therefore, XML documents constitute an important data mining domain. In this paper, we propose a new method of XML document clustering by a global criterion function, considering the weight of common structures. Our approach initially extracts representative structures of frequent patterns from schemaless XML documents using a sequential pattern mining algorithm. Then, we perform clustering of an XML document by the weight of common structures, without a measure of pairwise similarity, assuming that an XML document is a transaction and frequent structures extracted from documents are items of the transaction. We conducted experiments to compare our method with previous methods. The experimental results show the effectiveness of our approach.     

A Novelty-based Clustering Method for On-line Documents

In this paper, we describe a document clustering method called novelty-based document clustering. This method clusters documents based on similarity and novelty. The method assigns higher weights to recent documents than old ones and generates clusters with the focus on recent topics. The similarity function is derived probabilistically, extending the conventional cosine measure of the vector space model by incorporating a document forgetting model to produce novelty-based clusters. The clustering procedure is a variation of the K-means method. An additional feature of our clustering method is an incremental update facility, which is applied when new documents are incorporated into a document repository. Performance of the clustering method is examined through experiments. Experimental results show the efficiency and effectiveness of our method.     

Distributed collaborative Web document clustering using cluster keyphrase summaries

For the past few decades the mainstream data clustering technologies have been fundamentally based on centralized operation; data sets were of small manageable sizes, and usually resided on one site that belonged to one organization. Today, data is of enormous sizes and is usually located on distributed sites; the primary example being the Web. This created a need for performing clustering in distributed environments. Distributed clustering solves two problems: infeasibility of collecting data at a central site, due to either technical and/or privacy limitations, and intractability of traditional clustering algorithms on huge data sets. In this paper we propose a distributed collaborative clustering approach for clustering Web documents in distributed environments. We adopt a peer-to-peer model, where the main objective is to allow nodes in a network to first form independent opinions of local document clusterings, then collaborate with peers to enhance the local clusterings. Information exchanged between peers is minimized through the use of cluster summaries in the form of keyphrases extracted from the clusters. This summarized view of peer data enables nodes to request merging of remote data selectively to enhance local clusters. Initial clustering, as well as merging peer data with local clusters, utilizes a clustering method, called similarity histogram-based clustering, based on keeping a tight similarity distribution within clusters. This approach achieves significant improvement in local clustering solutions without the cost of centralized clustering, while maintaining the initial local clustering structure. Results show that larger networks exhibit larger improvements, up to 15% improvement in clustering quality, albeit lower absolute clustering quality than smaller networks.     

A three-phase approach to document clustering based on topic significance degree

Topic model can project documents into a topic space which facilitates effective document clustering. Selecting a good topic model and improving clustering performance are two highly correlated problems for topic based document clustering. In this paper, we propose a three-phase approach to topic based document clustering. In the first phase, we determine the best topic model and present a formal concept about significance degree of topics and some topic selection criteria, through which we can find the best number of the most suitable topics from the original topic model discovered by LDA. Then, we choose the initial clustering centers by using the k-means++ algorithm. In the third phase, we take the obtained initial clustering centers and use the k-means algorithm for document clustering. Three clustering solutions based on the three phase approach are used for document clustering. The related experiments of the three solutions are made for comparing and illustrating the effectiveness and efficiency of our approach.     

基于词条与语意差异度量的文档聚类算法

已有的文本聚类算法大多基于一般的相似性度量而忽略了语义内容,对此提出一种基于最大化文本判别信息的文本聚类算法。首先,分别分析词条对其类簇与其他类簇的判别信息,并且将数据集从输入空间转换至差异分数矩阵空间;然后,设计了一个贪婪算法来筛选矩阵每行的低分数词条;最终,采用最大似然估计对文本差别信息进行平滑处理。仿真实验结果表明,所提方法的文档聚类质量优于其他分层与单层聚类算法,并且具有较好的可解释性与收敛性。     

Document Similarity Using a Phrase Indexing Graph Model

Document clustering techniques mostly rely on single term analysis of text, such as the vector space model. To better capture the structure of documents, the underlying data model should be able to represent the phrases in the document as well as single terms. We present a novel data model, the Document Index Graph, which indexes Web documents based on phrases rather than on single terms only. The semistructured Web documents help in identifying potential phrases that when matched with other documents indicate strong similarity between the documents. The Document Index Graph captures this information, and finding significant matching phrases between documents becomes easy and efficient with such model. The model is flexible in that it could revert to a compact representation of the vector space model if we choose not to index phrases. However, using phrase indexing yields more accurate document similarity calculations. The similarity between documents is based on both single term weights and matching phrase weights. The combined similarities are used with standard document clustering techniques to test their effect on the clustering quality. Experimental results show that our phrase-based similarity, combined with single-term similarity measures, gives a more accurate measure of document similarity and thus significantly enhances Web document clustering quality.