An Intelligent Information System for Organizing Online Text Documents

This paper describes an intelligent information system for effectively managing huge amounts of online text documents (such as Web documents) in a hierarchical manner. The organizational capabilities of this system are able to evolve semi-automatically with minimal human input. The system starts with an initial taxonomy in which documents are automatically categorized, and then evolves so as to provide a good indexing service as the document collection grows or its usage changes. To this end, we propose a series of algorithms that utilize text-mining technologies such as document clustering, document categorization, and hierarchy reorganization. In particular, clustering and categorization algorithms have been intensively studied in order to provide evolving facilities for hierarchical structures and categorization criteria. Through experiments using the Reuters-21578 document collection, we evaluate the performance of the proposed clustering and categorization methods by comparing them to those of well-known conventional methods.     

A Latent Semantic Indexing-based approach to multilingual document clustering

The creation and deployment of knowledge repositories for managing, sharing, and reusing tacit knowledge within an organization has emerged as a prevalent approach in current knowledge management practices. A knowledge repository typically contains vast amounts of formal knowledge elements, which generally are available as documents. To facilitate users' navigation of documents within a knowledge repository, knowledge maps, often created by document clustering techniques, represent an appealing and promising approach. Various document clustering techniques have been proposed in the literature, but most deal with monolingual documents (i.e., written in the same language). However, as a result of increased globalization and advances in Internet technology, an organization often maintains documents in different languages in its knowledge repositories, which necessitates multilingual document clustering (MLDC) to create organizational knowledge maps. Motivated by the significance of this demand, this study designs a Latent Semantic Indexing (LSI)-based MLDC technique capable of generating knowledge maps (i.e., document clusters) from multilingual documents. The empirical evaluation results show that the proposed LSI-based MLDC technique achieves satisfactory clustering effectiveness, measured by both cluster recall and cluster precision, and is capable of maintaining a good balance between monolingual and cross-lingual clustering effectiveness when clustering a multilingual document corpus.     

基于搜索结果的个性化推荐系统研究

搜索引擎返回的信息太多且不能根据用户的兴趣提供检索结果,使得用户使用搜索引擎难以用简便的方式找到感兴趣的文档。个性化推荐是一种旨在减轻用户在信息检索方面负担的有效方法。文中把内容过滤技术和文档聚类技术相结合,实现了一个基于搜索结果的个性化推荐系统,以聚类的方法自动组织搜索结果,主动推荐用户感兴趣的文档。通过建立用户概率兴趣模型,对搜索结果STC聚类的基础上进行内容过滤。实验表明,概率模型比矢量空间模型更好地表达了用户的兴趣和变化。     

Distributed Web Log Mining Using Maximal Large Itemsets

We introduce a partitioning-based distributed document-clustering algorithm using user access patterns from multi-server web sites. Our algorithm makes it possible to exploit simultaneously adaptive document replication and persistent connections, two techniques that are most effective in decreasing the response time that is observed by web users. The algorithm first distributes the user access data evenly among the servers by using a hash function. Then, each server generates a local clustering on its fair share of the user sessions records by employing a traditional single-machine document-clustering algorithm. Finally, those local clustering results are combined together by using a novel procedure that generates maximal large itemsets of web documents. We present preliminary experimental results and discuss alternative approaches to be pursued in the future. Received 30 August 2000 / Revised 30 January 2001 / Accepted in revised form 9 May 2001     

Personalized Concept-Based Clustering of Search Engine Queries

The exponential growth of information on the Web has introduced new challenges for building effective search engines. A major problem of web search is that search queries are usually short and ambiguous, and thus are insufficient for specifying the precise user needs. To alleviate this problem, some search engines suggest terms that are semantically related to the submitted queries so that users can choose from the suggestions the ones that reflect their information needs. In this paper, we introduce an effective approach that captures the user's conceptual preferences in order to provide personalized query suggestions. We achieve this goal with two new strategies. First, we develop online techniques that extract concepts from the web-snippets of the search result returned from a query and use the concepts to identify related queries for that query. Second, we propose a new two-phase personalized agglomerative clustering algorithm that is able to generate personalized query clusters. To the best of the authors' knowledge, no previous work has addressed personalization for query suggestions. To evaluate the effectiveness of our technique, a Google middleware was developed for collecting clickthrough data to conduct experimental evaluation. Experimental results show that our approach has better precision and recall than the existing query clustering methods.     

Selection criteria for text mining approaches

Text mining techniques include categorization of text, summarization, topic detection, concept extraction, search and retrieval, document clustering, etc. Each of these techniques can be used in finding some non-trivial information from a collection of documents. Text mining can also be employed to detect a document’s main topic/theme which is useful in creating taxonomy from the document collection. Areas of applications for text mining include publishing, media, telecommunications, marketing, research, healthcare, medicine, etc. Text mining has also been applied on many applications on the World Wide Web for developing recommendation systems. We propose here a set of criteria to evaluate the effectiveness of text mining techniques in an attempt to facilitate the selection of appropriate technique.     

减聚类的模糊C-均值算法在文本分类中的应用

首先,选择合适的文本集合,并且对文本进行分词处理,然后,进行文档内部特征词的提取,通过采用词频统计的方法对文本向量进行降维处理,从而选择最佳的特征向量。最后,将非数值的文本数据进行量化处理后,利用减聚类优化的模糊C-均值算法对文本集合进行聚类,从而提高文本聚类的效果。     

On using partial supervision for text categorization

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.     

基于非负矩阵分解的双重约束文本聚类算法

提出一种基于非负矩阵分解(NMF)的双重约束文本聚类算法。在正交三重NMF模型中,加入文本空间的成对约束信息和词空间的类别约束信息,将不同的特征词项进行分类。利用迭代规则对原始的词-文档矩阵进行分解,获得文本聚类结果。与多种传统半监督文本聚类算法的对比结果表明,该算法具有较高的聚类精度,能提供更准确和有效的聚类结果。     

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.     

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 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.     

基于单词超团的文本聚类方法

为优化文本聚类效果,提出一种基于单词超团理论的文本聚类方法.利用文档中单词的关联模式来评估文档间的相似度,将单词超团作为文档向量辅助信息,以图划分的方式进行聚类分析.对不同聚类方法的结果进行比较,证明基于单词超团的文本聚类方法能提高文本聚类的准确性.     

Performance evaluation of density-based clustering methods

With the development of the World Wide Web, document clustering is receiving more and more attention as an important and fundamental technique for unsupervised document organization, automatic topic extraction, and fast information retrieval or filtering. A good document clustering approach can assist computers in organizing the document corpus automatically into a meaningful cluster hierarchy for efficient browsing and navigation, which is very valuable for complementing the deficiencies of traditional information retrieval technologies. In this paper, we study the performance of different density-based criterion functions, which can be classified as internal, external or hybrid, in the context of partitional clustering of document datasets. In our study, a weight was assigned to each document, which defined its relative position in the entire collection. To show the efficiency of the proposed approach, the weighted methods were compared to their unweighted variants. To verify the robustness of the proposed approach, experiments were conducted on datasets with a wide variety of numbers of clusters, documents and terms. To evaluate the criterion functions, we used the WebKb, Reuters-21578, 20Newsgroups-18828, WebACE and TREC-5 datasets, as they are currently the most widely used benchmarks in document clustering research. To evaluate the quality of a clustering solution, a wide spectrum of indices, three internal validity indices and seven external validity indices, were used. The internal validity indices were used for evaluating the within-cluster scatter and between cluster separations. The external validity indices were used for comparing the clustering solutions produced by the proposed criterion functions with the “ground truth” results. Experiments showed that our approach significantly improves clustering quality. In this paper, we developed a modified differential evolution (DE) algorithm to optimize the criterion functions. This modification accelerates the convergence of DE and, unlike the basic DE algorithm, guarantees that the received solution will be feasible.     

用于文档聚类的间隔流形学习算法研究

为有效解决文档聚类问题,提出一种基于间隔流形学习的文档聚类算法。该算法利用间隔Fisher分析将高维文档空间降维到低维特征空间,利用支持向量聚类算法进行聚类。在基准文档测试集上的实验结果表明,该算法的聚类性能优于其他常用的文档聚类算法。     

Design and Development of Density-Based Effective Document Clustering Method Using Ontology

Text document clustering is used to separate a collection of documents into several clusters by allowing the documents in a cluster to be substantially similar. The documents in one cluster are distinct from documents in other clusters. The high-dimensional sparse document term matrix reduces the clustering process efficiency. This study proposes a new way of clustering documents using domain ontology and WordNet ontology. The main objective of this work is to increase cluster output quality. This work aims to investigate and examine the method of selecting feature dimensions to minimize the features of the document name matrix. The sports documents are clustered using conventional K-Means with the dimension reduction features selection process and density-based clustering. A novel approach named ontology-based document clustering is proposed for grouping the text documents. Three critical steps were used in order to develop this technique. The initial step for an ontology-based clustering approach starts with data pre-processing, and the characteristics of the DR method are reduced with the Info-Gain collection. The documents are clustered using two clustering methods: K-Means and Density-Based clustering with DR Feature Selection Process. These methods validate the findings of ontology-based clustering, and this study compared them using the measurement metrics. The second step of this study examines the sports field ontology development and describes the principles and relationship of the terms using sports-related documents. The semantic web rational process is used to test the ontology for validation purposes. An algorithm for the synonym retrieval of the sports domain ontology terms has been proposed and implemented. The retrieved terms from the documents and sport ontology concepts are mapped to the retrieved synonym set words from the WorldNet ontology. The suggested technique is based on synonyms of mapped concepts. The proposed ontology approach employs the reduced feature set in order to clustering the text documents. The results are compared with two traditional approaches on two datasets. The proposed ontology-based clustering approach is found to be effective in clustering the documents with high precision, recall, and accuracy. In addition, this study also compared the different RDF serialization formats for sports ontology.

     

Modeling user interests by conceptual clustering

As more information becomes available on the Web, there has been a crescent interest in effective personalization techniques. Personal agents providing assistance based on the content of Web documents and the user interests emerged as a viable alternative to this problem. Provided that these agents rely on having knowledge about users contained into user profiles, i.e., models of user preferences and interests gathered by observation of user behavior, the capacity of acquiring and modeling user interest categories has become a critical component in personal agent design. User profiles have to summarize categories corresponding to diverse user information interests at different levels of abstraction in order to allow agents to decide on the relevance of new pieces of information. In accomplishing this goal, document clustering offers the advantage that an a priori knowledge of categories is not needed, therefore the categorization is completely unsupervised. In this paper we present a document clustering algorithm, named WebDCC (Web Document Conceptual Clustering), that carries out incremental, unsupervised concept learning over Web documents in order to acquire user profiles. Unlike most user profiling approaches, this algorithm offers comprehensible clustering solutions that can be easily interpreted and explored by both users and other agents. By extracting semantics from Web pages, this algorithm also produces intermediate results that can be finally integrated in a machine-understandable format such as an ontology. Empirical results of using this algorithm in the context of an intelligent Web search agent proved it can reach high levels of accuracy in suggesting Web pages.     

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.     

基于分级神经网络的Web文档模糊聚类技术

给出了一种多层向量空间模型,该模型将一篇文档的相关信息从逻辑上划分为多个相对独立的文本段,按照不同位置的文本段确定相应的索引项权重．然后提出了一种简明而有效的基于分级神经网络的模糊聚类算法．与现有方法不同,该模糊聚类方法采用自组织神经网络和模糊聚类网络两部分组成的3层神经网络来实现．首先采用自组织神经网络从原始数据产生一个初始聚类结果,然后运用FCM方法对初始聚类的数目进行优化．实验结果表明,提出的Web文档聚类算法具有较好的聚类特性,它能将与一个主题相关的web文档较完全和准确地聚成一类．