Data Mining Practical Machine Learning Tools and Techniques

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

The Visual Display of Quantitative Information

Data Nodes, Edges Display Interactive Display Visual Analogues VisualItems in ItemRegistry User Figure 6.3: The Information Visualisation Reference Model, adapted from Heer et al.[57] 6.2 State of the Art 93 a visual analytics issue that should be better tackled by all the visualisation communities. Blending different kinds of visualisations in the same application is becoming Blending different kinds of visualisations is currently difficult more frequent. Scientific visualisation and geographic visualisation need information visualisation because they manage multi-valued data with complex topologies that can be visualised using their canonical geometry. In addition, they can also be explored with more abstract visual representations to avoid geometric artefacts. For example, census data can be visualised as a coloured map but also as a multi-dimensional dataset where the longitude and latitude are two attributes among others. Clustering this data by some similarity measure will then reveal places that can be far away in space but behave similarly in term of other attributes (e.g., level of education, level of income, size of houses etc.), similarity that would not be visible on a map. On top of these visualisation systems, a user interface allows control of the overall application. User interfaces are well understood but they can be very different in styles. 3D systems use specific types of interfaces that are very different to traditional desktop interfaces. Moreover, information visualisation systems tend to deeply embed the interaction with the visualisation, offering special kinds of controls either directly inside the visualisations (e.g., range sliders on the axes of parallel coordinates) or around it but with special kinds of widgets (e.g., range sliders for performing range-queries). Interoperability can thus be described at several levels. At the data management level, at the architecture model level and at the interface level. 6.2.2 Data Management All visual analytics applications start with data that can be either statically collected or dynamically produced. Depending on the nature of the data, visual analytics applications have used various ways of managing their storage. In order of sophistication, they are: Flat files using ad-hoc formats, Structured file formats such as XML, Specialised NoSQL systems, including Cloud Storage, Standard or extended transactional databases (SQL), Workflow or dataflow systems integrating storage, distribution and data processing. We will now consider these data storage methods, paying particular attention to Data Management for visual analytics can rely on different levels of sophistication the levels of service required by visual analytics, such as: Persistence (they all provide it by definition), Typing, Distribution, Atomic transactions, Notification, Interactive performance, Computation.

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Mastering the information age : solving problems with visual analytics

We must all accept that science is data and that data are science, and thus provide for, and justify the need for the support of, much-improved data curation. (Hanson, Sugden, & Alberts) 
 
Researchers are producing an unprecedented deluge of data by using new methods and instrumentation. Others may wish to mine these data for new discoveries and innovations. However, research data are not readily available as sharing is common in only a few fields such as astronomy and genomics. Data sharing practices in other fields vary widely. Moreover, research data take many forms, are handled in many ways, using many approaches, and often are difficult to interpret once removed from their initial context. Data sharing is thus a conundrum. Four rationales for sharing data are examined, drawing examples from the sciences, social sciences, and humanities: (1) to reproduce or to verify research, (2) to make results of publicly funded research available to the public, (3) to enable others to ask new questions of extant data, and (4) to advance the state of research and innovation. These rationales differ by the arguments for sharing, by beneficiaries, and by the motivations and incentives of the many stakeholders involved. The challenges are to understand which data might be shared, by whom, with whom, under what conditions, why, and to what effects. Answers will inform data policy and practice. © 2012 Wiley Periodicals, Inc.

科研数据共享的挑战 (The Conundrum of Sharing Research Data)

Ugr'16: a new dataset for the evaluation of cyclostationarity-based network IDSs

Tap into visual analysis of customization of grouping of activities in eLearning

Discovering usage behaviors and engagement in an Educational Virtual World

Summary: BicOverlapper is a tool to visualise biclusters from gene expression matrices in a way that helps to compare biclustering methods, to unravel trends and to highlight relevant genes and conditions. A visual approach can complement biological and statistical analysis and reduce the time spent by specialists interpreting the results of biclustering algorithms. The technique is based on a force-directed graph where biclusters are represented as flexible overlapped groups of genes and conditions. Availability: The BicOverlapper software and supplementary material are available at http://vis.usal.es/bicoverlapper.

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BicOverlapper: a tool for bicluster visualization.

Motivation: Although natural products represent a reservoir of molecular diversity, the process of isolating and identifying active compounds is a bottleneck in drug discovery programs. The rapid isolation and identification of the bioactive component(s) of natural product mixtures during the bioassay-guided fractionation have become crucial factors in the competition with chemical compound libraries and combinatorial synthetic efforts. In this respect, the use of spectral databases in identification processes is indispensable.

Results: We have developed a database containing 13C spectral information of over 6000 natural compounds, which allows for fast identifications of known compounds present in the crude extracts and provides insight into the structural elucidation of unknown compounds.

Availability: http://c13.usal.es 

Contact: [email protected]

Roberto Therón

Papers

Ugr'16: a new dataset for the evaluation of cyclostationarity-based network IDSs

Tap into visual analysis of customization of grouping of activities in eLearning

Discovering usage behaviors and engagement in an Educational Virtual World

BicOverlapper: a tool for bicluster visualization.

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