Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications


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In this setting, states of nature of the application area under consideration are described by Boolean vectors de ned on some attributes.


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That is, by data points de ned in the Boolean space of the attributes. It is postulated that there exists a partition of this space into two classes, which should be inferred as patterns on the attributes when only several data points are known, the so-called positive and negative training examples. In other words, to infer the binary value of one more attribute, called the goal or class attribute. To solve this problem, some methods have been suggested which construct a Boolean function separating the two given sets of positive and negative training data points.

Toon meer Toon minder. Verschijningsdatum september Aantal pagina's pagina's Illustraties Nee. Reviews Schrijf een review. Kies je bindwijze. Verkoop door bol. Recently, machine learning has been widely adopted in search and advertising, mainly due to the availability of huge amount of interaction data between users, advertisers, and search engines.

In this talk, we discuss how to use machine learning to build effective ranking models which we call learning to rank and to optimize auction mechanisms. To tackle it, we propose the so-called listwise ranking algorithms, whose loss functions are defined on the permutations of documents, instead of individual documents or document pairs. We prove the effectiveness of these algorithms by analyzing their generalization ability and statistical consistency, based on the assumption of a two-layer probabilistic sampling procedure for queries and documents, and the characterization of the relationship between their loss functions and the evaluation measures used by search engines e.

To tackle this challenge, we propose a game-theoretic learning method, which first models the strategic behaviors of advertisers, and then optimizes the auction mechanism by assuming the advertisers to respond to new auction mechanisms according to the learned behavior model. We prove the effectiveness of the proposed method by analyzing the generalization bounds for both behavior learning and auction mechanism learning based on a novel Markov framework.

His research interests include machine learning learning to rank, online learning, statistical learning theory, and deep learning , algorithmic game theory, and computational economics. He is well known for his work on learning to rank for information retrieval. He has authored the first book in this area, and published tens of highly-cited papers on both algorithms and theorems of learning to rank. He has also published extensively on other related topics. In particular, his paper won the best student paper award of SIGIR , and the most cited paper award of the Journal of Visual Communication and Image Representation ; his group won the research break-through award of Microsoft Research Asia Tie-Yan is very active in serving the research community.

Francis Bach Senior Researcher INRIA, Paris Title : Beyond stochastic gradient descent for large-scale machine learning Abstract : Many machine learning and signal processing problems are traditionally cast as convex optimization problems. In this setting, online algorithms such as stochastic gradient descent which pass over the data only once, are usually preferred over batch algorithms, which require multiple passes over the data.

PDF Presentation: Beyond stochastic gradient descent for large-scale machine learning. He completed his Ph. Francis Bach is interested in statistical machine learning, and especially in graphical models, sparse methods, kernel-based learning, convex optimization vision and signal processing. Electric power meters measure and transmit to a central information system electric power consumption from every individual household or enterprise. The sampling rate may vary from 10 minutes to 24 hours and the latency to reach the central information system may vary from a few minutes to 24h.

This generates a large amount of — possibly streaming — data if we consider customers from an entire country ex. This data is collected firstly for billing purposes but can be processed with data analytics tools with several other goals. The first part of the talk will recall the structure of electric power smart metering data and review the different applications which are considered today for applying data analytics to such data. In a second part of the talk, we will focus on a specific problem: spatio-temporal estimation of aggregated electric power consumption from incomplete metering data.

PDF Presentation: Making smart metering smarter by applying data analytics. His background is in Business Intelligence covering many aspects from data storage and querying to data analytics. From to , he was a professor of computer science at Telecom ParisTech, teaching and doing research in the field of information systems and business intelligence, with a focus on time series management, stream processing and mining. His current research interest is on distributed and privacy-preserving data mining on electric power related data.

This new landscape is particularly interesting for online e-commerce players where user actions can also be measured online and thus allow for a complete measure of return on ad-spend. Despite those benefits, new challenges need to be addressed such as : — the design of a realtime bidding architecture handling high volumes of queries at low latencies, — the exploration of a sparse and volatile high-dimensional space — as well as several statistical modeling problems e.


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  6. In this talk, I will present an approach to realtime media buying for online e-commerce from our experience working in the field. I will review the aforementioned challenges and discuss open problems for serving ads that matter. Twenga is a services and solutions provider generating high value-added leads to online merchants that was founded in With over 14 years of experience, Alexandre has held a succession of increasingly responsible positions focusing on advertising and web development.

    Prior to joining Twenga, he was responsible for the development of Search and Advertising at Orange. Alexandre graduated from Ecole polytechnique. Database and Expert Systems Applications:. Seminal Contributions to Information Systems Engineering. July ; Izmir, Turkey. Jurack S, Taentzer G: A component concept for typed graphs with inheritance and containment structures. Graph Transformations. Eurographics Symposium on Point-Based Graphics:. Inverse Probl. Keim D: Pixel-oriented visualization techniques for exploring very large databases. Journal of Computational and Graphical Statistics.

    Kosara R, Miksch S: Visualization methods for data analysis and planning in medical applications. International Journal of Medical Informatics. Inselberg A: Visualization of concept formation and learning.

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    Edited by: Holzinger A. Gehlenborg N, Brazma A: Visualization of large microarray experiments with space maps. Human-Computer Interaction Series. Annual Review of Psychology, Vol Palo Alto: Annual Reviews. Computing and Informatics. Pattern Recognit Lett. J Univers Comput Sci. Beale R: Supporting serendipity: Using ambient intelligence to augment user exploration for data mining and Web browsing.

    International Winter School on Big Data

    International Journal of Human-Computer Studies. Med Phys. High Performance Computing Symposium Edited by: Tochtermann K, Maurer H. Williams AJ, Ekins S, Tkachenko V: Towards a gold standard: regarding quality in public domain chemistry databases and approaches to improving the situation. Drug Discovery Today. Ieee Transactions on Knowledge and Data Engineering. J: Integration, visualization and analysis of human interactome.

    Computer Science and Knowledge Discovery

    Biochemical and Biophysical Research Communications. Nucleic acids research. Download references. Correspondence to Andreas Holzinger.

    Lecture - 34 Data Mining and Knowledge Discovery

    This article is published under license to BioMed Central Ltd. Reprints and Permissions. Search all BMC articles Search.

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    Article metrics Accesses 96 Citations 2 Altmetric Metrics details. Computers are incredibly fast, accurate, and stupid. Human beings are incredibly slow, inaccurate, and brilliant. Together they are powerful beyond imagination. Background The life sciences, biomedicine and health care are increasingly turning into a data intensive science [ 2 — 4 ]. Figure 1. Full size image. Knowledge Discovery process The traditional method of turning data into knowledge relied on manual analysis and interpretation by a domain expert in order to find useful patterns in data for decision support.

    Figure 2. The knowledge discovery process in the life sciences. Future research directions Figure 2 illustrates the complete knowledge discovery process, and we will use this "big picture" for the description of some problems and challenges - starting in this Figure from right to left - from the computer to the human - segmenting it into four large areas: Area 1: Interactive data integration, data fusion and pre-selection of data sets Many different biological species humans, animals, bacteria, virus, plants, Area 3: Interactive advanced data mining methods, pattern discovery Many data mining methods are designed for collections of objects well-represented in rigid tabular formats.

    Advanced data mining approaches include: 1 graph-based data mining [ 78 ], [ 79 ], [ 80 ], [ 81 ], 2 entropy-based data mining [ 47 , 82 ], [ 83 — 85 ], and 3 topological data mining [ 86 , 87 ]. Horizontal area: Privacy, data protection, data security, data safety Whenever we deal with biomedical data issues of privacy, data protection, data security and data safety and the fair use of data are of paramount importance [ ], including data accessibility, temporal limits, legal restrictions such as in situations where copyright or patents may be relevant , confidentiality and data provenance.

    Additional aspects to consider Some additional aspects to consider include: Cross-disciplinary cooperation with domain experts Building a project consortium comprising of experts with complementary expertise but common interests is a success factor in each project. Interpretability As we broaden workflows for data mining, we have to expand metrics used to evaluate our results. Benchmarking against Gold-Standards To measure the quality of data mining approaches, the production of benchmarks it very important.

    Reproducibility A big general issue among our modern research communities is that rarely one can reproduce the results of other researchers. Embedded data mining Whilst existing research has shown the value of data-driven science, we need to further integrate knowledge discovery and visualization pipelines into biological and biomedical and especially clinical workflows to take full advantage of their potential [ 23 ]. Complexity of data analysis methods Deciding which method is the most suitable for solving a particular data analysis problem is often critical as the interdependencies make the selection non-linear [ ].

    Conclusion We are just at the beginning of a turning point towards data intensive life sciences, which entails many challenges and future research directions. References 1. Google Scholar 4. Google Scholar 6. PubMed Google Scholar 7. Google Scholar 8. Google Scholar 9. PubMed Google Scholar Google Scholar CAS Google Scholar July ; Izmir, Turkey Google Scholar PubMed Central Google Scholar Declarations Publication for this article has been funded by the Research Unit hci4all.

    Additional information Competing interests All authors declare that they have no competing interests. About this article. Contact us Submission enquiries: bmcbioinformatics biomedcentral.

    Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications
    Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications
    Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications
    Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications
    Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications
    Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications
    Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications
    Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications

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