From visual data exploration to visual data mining: a survey.

We survey work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data. Basic terminology related to data mining, data sets, and visualization is introduced. Previous work on information visualization is reviewed in light of different categorizations of techniques and systems. The role of interaction techniques is discussed, in addition to work addressing the question of selecting and evaluating visualization techniques. We review some representative work on the use of information visualization techniques in the context of mining data. This includes both visual data exploration and visually expressing the outcome of specific mining algorithms. We also review recent innovative approaches that attempt to integrate visualization into the DM/KDD process, using it to enhance user interaction and comprehension

Incorporating semantic and syntactic information into document representation for document clustering

Document clustering is a widely used strategy for information retrieval and text data mining. In traditional document clustering systems, documents are represented as a bag of independent words. In this project, we propose to enrich the representation of a document by incorporating semantic information and syntactic information. Semantic analysis and syntactic analysis are performed on the raw text to identify this information. A detailed survey of current research in natural language processing, syntactic analysis, and semantic analysis is provided. Our experimental results demonstrate that incorporating semantic information and syntactic information can improve the performance of our document clustering system for most of our data sets. A statistically significant improvement can be achieved when we combine both syntactic and semantic information. Our experimental results using compound words show that using only compound words does not improve the clustering performance for our data sets. When the compound words are combined with original single words, the combined feature set gets slightly better performance for most data sets. But this improvement is not statistically significant. In order to select the best clustering algorithm for our document clustering system, a comparison of several widely used clustering algorithms is performed. Although the bisecting K-means method has advantages when working with large datasets, a traditional hierarchical clustering algorithm still achieves the best performance for our small datasets

Text mining techniques for patent analysis.

Abstract Patent documents contain important research results. However, they are lengthy and rich in technical terminology such that it takes a lot of human efforts for analyses. Automatic tools for assisting patent engineers or decision makers in patent analysis are in great demand. This paper describes a series of text mining techniques that conforms to the analytical process used by patent analysts. These techniques include text segmentation, summary extraction, feature selection, term association, cluster generation, topic identification, and information mapping. The issues of efficiency and effectiveness are considered in the design of these techniques. Some important features of the proposed methodology include a rigorous approach to verify the usefulness of segment extracts as the document surrogates, a corpus-and dictionary-free algorithm for keyphrase extraction, an efficient co-word analysis method that can be applied to large volume of patents, and an automatic procedure to create generic cluster titles for ease of result interpretation. Evaluation of these techniques was conducted. The results confirm that the machine-generated summaries do preserve more important content words than some other sections for classification. To demonstrate the feasibility, the proposed methodology was applied to a realworld patent set for domain analysis and mapping, which shows that our approach is more effective than existing classification systems. The attempt in this paper to automate the whole process not only helps create final patent maps for topic analyses, but also facilitates or improves other patent analysis tasks such as patent classification, organization, knowledge sharing, and prior art searches

Deliverable D1.1 State of the art and requirements analysis for hypervideo

This deliverable presents a state-of-art and requirements analysis report for hypervideo authored as part of the WP1 of the LinkedTV project. Initially, we present some use-case (viewers) scenarios in the LinkedTV project and through the analysis of the distinctive needs and demands of each scenario we point out the technical requirements from a user-side perspective. Subsequently we study methods for the automatic and semi-automatic decomposition of the audiovisual content in order to effectively support the annotation process. Considering that the multimedia content comprises of different types of information, i.e., visual, textual and audio, we report various methods for the analysis of these three different streams. Finally we present various annotation tools which could integrate the developed analysis results so as to effectively support users (video producers) in the semi-automatic linking of hypervideo content, and based on them we report on the initial progress in building the LinkedTV annotation tool. For each one of the different classes of techniques being discussed in the deliverable we present the evaluation results from the application of one such method of the literature to a dataset well-suited to the needs of the LinkedTV project, and we indicate the future technical requirements that should be addressed in order to achieve higher levels of performance (e.g., in terms of accuracy and time-efficiency), as necessary

Large-Scale Pattern-Based Information Extraction from the World Wide Web

Extracting information from text is the task of obtaining structured, machine-processable facts from information that is mentioned in an unstructured manner. It thus allows systems to automatically aggregate information for further analysis, efficient retrieval, automatic validation, or appropriate visualization. This work explores the potential of using textual patterns for Information Extraction from the World Wide Web

PERICLES Deliverable 4.3:Content Semantics and Use Context Analysis Techniques

The current deliverable summarises the work conducted within task T4.3 of WP4, focusing on the extraction and the subsequent analysis of semantic information from digital content, which is imperative for its preservability. More specifically, the deliverable defines content semantic information from a visual and textual perspective, explains how this information can be exploited in long-term digital preservation and proposes novel approaches for extracting this information in a scalable manner. Additionally, the deliverable discusses novel techniques for retrieving and analysing the context of use of digital objects. Although this topic has not been extensively studied by existing literature, we believe use context is vital in augmenting the semantic information and maintaining the usability and preservability of the digital objects, as well as their ability to be accurately interpreted as initially intended.PERICLE

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin