A New Evolving Tree-Based Model with Local Re-learning for Document Clustering and Visualization

The Evolving tree (ETree) is a hierarchical clustering and visualization model that allows the number of clusters to grow and evolve with new data samples in an online learning manner. While many hierarchical clustering models are available in the literature, ETree stands out because of its visualization capability. It is an enhancement of the Self-Organizing Map, a famous and useful clustering and visualization model. ETree organises the trained data samples in the form of a tree structure for better presentation and visualization especially for high-dimensional data samples. Even though ETree has been used in a number of applications, its use in textual document clustering and visualization is limited. In this paper, ETree is modified and deployed as a useful model for undertaking textual documents clustering and visualization problems. We introduce a new local re-learning procedure that allows the tree structure to grow and adapt to new features, i.e., new words from new textual documents. The performance of the proposed ETree model is evaluated with two (one benchmark and one real) document data sets. A number of key aspects of the proposed ETree model, which include its topology representation, learning time, as well as recall and precision rates, are evaluated. The results show that the proposed local re-learning procedure is useful for handling increasing number of features incrementally. In summary, this study contributes towards a modified ETree model and its use in a new domain, i.e., textual document clustering and visualization

Adaptive content mapping for internet navigation

The Internet as the biggest human library ever assembled keeps on growing. Although all kinds of information carriers (e.g. audio/video/hybrid file formats) are available, text based documents dominate. It is estimated that about 80% of all information worldwide stored electronically exists in (or can be converted into) text form. More and more, all kinds of documents are generated by means of a text processing system and are therefore available electronically. Nowadays, many printed journals are also published online and may even discontinue to appear in print form tomorrow. This development has many convincing advantages: the documents are both available faster (cf. prepress services) and cheaper, they can be searched more easily, the physical storage only needs a fraction of the space previously necessary and the medium will not age. For most people, fast and easy access is the most interesting feature of the new age; computer-aided search for specific documents or Web pages becomes the basic tool for information-oriented work. But this tool has problems. The current keyword based search machines available on the Internet are not really appropriate for such a task; either there are (way) too many documents matching the specified keywords are presented or none at all. The problem lies in the fact that it is often very difficult to choose appropriate terms describing the desired topic in the first place. This contribution discusses the current state-of-the-art techniques in content-based searching (along with common visualization/browsing approaches) and proposes a particular adaptive solution for intuitive Internet document navigation, which not only enables the user to provide full texts instead of manually selected keywords (if available), but also allows him/her to explore the whole database

Visualizing the semantic content of large text databases using text maps

A methodology for generating text map representations of the semantic content of text databases is presented. Text maps provide a graphical metaphor for conceptualizing and visualizing the contents and data interrelationships of large text databases. Described are a set of experiments conducted against the TIPSTER corpora of Wall Street Journal articles. These experiments provide an introduction to current work in the representation and visualization of documents by way of their semantic content

Visualising the structure of document search results: A comparison of graph theoretic approaches

This is the post-print of the article - Copyright @ 2010 Sage PublicationsPrevious work has shown that distance-similarity visualisation or ‘spatialisation’ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or ‘cluster growing’ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic owing to their inherent high dimensionality and non-linearity. Conventional linear approaches to dimension reduction tend to fail at this kind of task, sacrificing local structural in order to preserve a globally optimal mapping. In this paper the clustering performance of a recently proposed algorithm called isometric feature mapping (Isomap), which deals with non-linearity by transforming dissimilarities into geodesic distances, is compared to that of non-metric multidimensional scaling (MDS). Various graph pruning methods, for geodesic distance estimation, are also compared. Results show that Isomap is significantly better at preserving local structural detail than MDS, suggesting it is better suited to cluster growing and other semantic navigation tasks. Moreover, it is shown that applying a minimum-cost graph pruning criterion can provide a parameter-free alternative to the traditional K-neighbour method, resulting in spatial clustering that is equivalent to or better than that achieved using an optimal-K criterion

ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System

Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. With that aim in mind, the present work presents a self-organized ant colony based intrusion detection system (ANTIDS) to detect intrusions in a network infrastructure. The performance is compared among conventional soft computing paradigms like Decision Trees, Support Vector Machines and Linear Genetic Programming to model fast, online and efficient intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special track at WSTST 2005, Muroran, JAPA

TweeProfiles4: a weighted multidimensional stream clustering algorithm

O aparecimento das redes sociais abriu aos utilizadores a possibilidade de facilmente partilharem as suas ideias a respeito de diferentes temas, o que constitui uma fonte de informação enriquecedora para diversos campos. As plataformas de microblogging sofreram um grande crescimento e de forma constante nos últimos anos. O Twitter é o site de microblogging mais popular, tornando-se uma fonte de dados interessante para extração de conhecimento. Um dos principais desafios na análise de dados provenientes de redes sociais é o seu fluxo, o que dificulta a aplicação de processos tradicionais de data mining. Neste sentido, a extração de conhecimento sobre fluxos de dados tem recebido um foco significativo recentemente. O TweeProfiles é a uma ferramenta de data mining para análise e visualização de dados do Twitter sobre quatro dimensões: espacial (a localização geográfica do tweet), temporal (a data de publicação do tweet), de conteúdo (o texto do tweet) e social (o grafo dos relacionamentos). Este é um projeto em desenvolvimento que ainda possui muitos aspetos que podem ser melhorados. Uma das recentes melhorias inclui a substituição do algoritmo de clustering original, o qual não suportava o fluxo contínuo dos dados, por um método de streaming. O objetivo desta dissertação passa pela continuação do desenvolvimento do TweeProfiles. Em primeiro lugar, será proposto um novo algoritmo de clustering para fluxos de dados com o objetivo de melhorar o existente. Para esse efeito será desenvolvido um algoritmo incremental com suporte para fluxos de dados multi-dimensionais. Esta abordagem deve permitir ao utilizador alterar dinamicamente a importância relativa de cada dimensão do processo de clustering. Adicionalmente, a avaliação empírica dos resultados será alvo de melhoramento através da identificação e implementação de medidas adequadas de avaliação dos padrões extraídos. O estudo empírico será realizado através de tweets georreferenciados obtidos pelo SocialBus.The emergence of social media made it possible for users to easily share their thoughts on different topics, which constitutes a rich source of information for many fields. Microblogging platforms experienced a large and steady growth over the last few years. Twitter is the most popular microblogging site, making it an interesting source of data for pattern extraction. One of the main challenges of analyzing social media data is its continuous nature, which makes it hard to use traditional data mining. Therefore, mining stream data has also received a lot of attention recently.TweeProfiles is a data mining tool for analyzing and visualizing Twitter data over four dimensions: spatial (the location of the tweet), temporal (the timestamp of the tweet), content (the text of the tweet) and social (relationship graph). This is an ongoing project which still has many aspects that can be improved. For instance, it was recently improved by replacing the original clustering algorithm which could not handle the continuous flow of data with a streaming method. The goal of this dissertation is to continue the development of TweeProfiles. First, the stream clustering process will be improved by proposing a new algorithm. This will be achieved by developing an incremental algorithm with support for multi-dimensional streaming data. Moreover, it should make it possible for the user to dynamically change the relative importance of each dimension in the clustering. Additionally, the empirical evaluation of the results will also be improved.Suitable measures to evaluate the extracted patterns will be identified and implemented. An empirical study will be done using data consisting of georeferenced tweets from SocialBus

Cluster to User Profile Ontology Mapping

In this paper, we present an approach that uses cluster analysis techniques to extend the ontology of an E-learning domain. This approach is significantly different from any current information retrieval systems, it uses a global ontology model that represents the whole E-learning domain combined with clusters’ centroids vocabularies (terms) to extend the core ontology model. The most important advantage of clustering from the personalization perspective is that the clusters are later used as automatically constructed labels for each user profile. Hence, depending on the document collection and its evolution, both the user profiles and their underlying ontology labels are allowed to change or evolve accordingly. Our proposed approach has been implemented on the HyperMany-Media1 platform at Western Kentucky University, USA