Data mining

This book constitutes the refereed proceedings of the 8th International Conference on Web Reasoning and Rule Systems, RR 2014, held in Athens, Greece in September 2014. The 9 full papers, 9 technical communications and 5 poster presentations presented together with 3 invited talks, 3 doctoral consortial papers were carefully reviewed and selected from 33 submissions. The conference covers a wide range of the following: semantic Web, rule and ontology languages, and related logics, reasoning, querying, searching and optimization, incompleteness, inconsistency and uncertainty, non-monotonic, common sense, and closed-world reasoning for the web, dynamic information, stream reasoning and complex event processing, decision making, planning, and intelligent agents, machine learning, knowledge extraction and information retrieval, data management, data integration and reasoning on the web of data, ontology-based data access, system descriptions, applications and experiences.

This book constitutes the refereed proceedings of the 17th International Conference on Engineering Applications of Neural Networks, EANN 2016, held in Aberdeen, UK, in September 2016.The 22 revised full papers and three short papers presented together with two tutorials were carefully reviewed and selected from 41 submissions. The papers are organized in topical sections on active learning and dynamic environments; semi-supervised modeling; classification applications; clustering applications; cyber-physical systems and cloud applications; time-series prediction; learning-algorithms.

This book constitutes the refereed proceedings of the 25th Australasian Database Conference, ADC 2014, held in Brisbane, NSW, Australia, in July 2014. The 15 full papers presented together with 6 short papers and 2 keynotes were carefully reviewed and selected from 38 submissions. A large variety of subjects are covered, including hot topics such as data warehousing; database integration; mobile databases; cloud, distributed, and parallel databases; high dimensional and temporal data; image/video retrieval and databases; database performance and tuning; privacy and security in databases; query processing and optimization; semi-structured data and XML; spatial data processing and management; stream and sensor data management; uncertain and probabilistic databases; web databases; graph databases; web service management; and social media data management.

This book constitutes the refereed proceedings of the 10th International Symposium on Bioinformatics Research and Applications, ISBRA 2014, held in Zhangjiajie, China, in June 2014. The 33 revised full papers and 31 one-page abstracts included in this volume were carefully reviewed and selected from 119 submissions. The papers cover a wide range of topics in bioinformatics and computational biology and their applications including the development of experimental or commercial systems.

This book constitutes the refereed proceedings of the 10th IEEE International Conference Beyond Databases, Architectures, and Structures, BDAS 2014, held in Ustron, Poland, in May 2014. This book consists of 56 carefully revised selected papers that are assigned to 11 thematic groups: query languages, transactions and query optimization; data warehousing and big data; ontologies and semantic web; computational intelligence and data mining; collective intelligence, scheduling, and parallel processing; bioinformatics and biological data analysis; image analysis and multimedia mining; security of database systems; spatial data analysis; applications of database systems; Web and XML in database systems.

Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the "e;noise"e; that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.

This book constitutes the proceedings of the 7th International Conference on Business Intelligence, CBI 2022, which took place in Khouribga, Morocco, during May 26-28, 2022. The 23 full papers included in this book were carefully reviewed and selected from a total of 68 submissions. They were organized in topical sections as follows: decision support and artificial intelligence; business intelligence and database; and optimization and dynamic programming.

This book constitutes the refereed proceedings of the 4th International Conference on Adaptive Instructional Systems, AIS 2022, held as part of the 23rd International Conference, HCI International 2022, which was held virtually in June/July 2022.The total of 1271 papers and 275 posters included in the HCII 2022 proceedings was carefully reviewed and selected from 5487 submissions.The AIS 2022 proceedings were organized in the following topical sections: Learner Modeling and State Assessment for Adaptive Instructional Decisions; Adaptation Design to Individual Learners and Teams; Design and Development of Adaptive Instructional Systems; Evaluating the Effectiveness of Adaptive Instructional Systems.

This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere.Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning;Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes;Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material.

This book constitutes the refereed proceedings of the 13th International ICT Innovations Conference, ICT Innovations 2021, held as virtual event in September 2021.The 15 full papers presented were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections on deep learning and AI; NLP and social network analysis; theoretical foundations and information security; e-services; sensor systems, IoT.

This book constitutes the refereed proceedings of the Third International Conference on Soft Computing and its Engineering Applications, icSoftComp 2021, held in Changa, India, in December 2021. Due to the COVID-19 pandemic the conference was held online. The 29 full papers and 4 short papers presented were carefully reviewed and selected from 247 submissions. The papers present recent research on theory and applications in fuzzy computing, neuro computing, and evolutionary computing.

Make any team or business data driven with this practical guide to overcoming common challenges and creating a data culture. Businesses are increasingly focusing on their data and analytics strategy, but a data-driven culture grounded in evidence-based decision making can be difficult to achieve. Be Data Driven outlines a step-by-step roadmap to building a data-driven organization or team, beginning with deciding on outcomes and a strategy before moving onto investing in technology and upskilling where necessary. This practical guide explains what it means to be a data-driven organization and explores which technologies are advancing data and analytics. Crucially, it also examines the most common challenges to becoming data driven, from a foundational skills gap to issues with leadership and strategy and the impact of organizational culture. With case studies of businesses who have successfully used data, Be Data Driven shows managers, leaders and data professionals how to address hurdles, encourage a data culture and become truly data driven.

In Data Cleaning for Effective Data Science, leading data science trainer David Mertz provides the most systematic guide to cleaning data for any project, using any library or toolset. Mertz introduces many powerful techniques for analyzing, manipulating, and pre-processing data sources. He offers best practices for working with leading data formats such as JSON, CSV, SQL RDBMSes, HDF5, NoSQL databases, files in image formats, binary serialized data structures, and more. Mertz also focuses on crucial issues within the data itself, including missing data, outliers, biasing trends, class imbalance, value imputation, over/under-sampling, normalization and/or randomization, and anomalies. This guide is organized around downloadable datasets, each illuminating specific issues with data integrity or quality. Each chapter explores the best ways to diagnose, analyze, and remediate these issues, offering hands-on practice using tools such as Python, Pandas, sklearn.preprocessing, scipy.stats, R, and Tidyverse. While the examples are demonstrated with widely-used tools, Mertz's concepts are applicable with any toolset. Each chapter also links to additional datasets with more problems, exercises, and solutions. Ancillary resources include Instructor Notes and PowerPoint lecture slides, which will both be downloadable from Pearson.com/us.

Neural Networks is an integral part in machine learning and a known tool for controlling nonlinear processes. The area is under rapid development and provides a tool for modelling and controlling of advanced processes. This book provides a comprehensive overview for modelling, simulation, measurement and control strategies for reactive distillations using neural networks.

This volume contains the proceedings of the third international conference on Pattern Recognition and Machine Intelligence (PReMI 2009) which was held at the Indian Institute of Technology, New Delhi, India, during December 16-20, 2009. This was the third conference in the series. The first two conferences were held in December at the Indian Statistical Institute, Kolkata in 2005 and 2007. PReMI has become a premier conference in India presenting state-of-art research findings in the areas of machine intelligence and pattern recognition. The conference is also successful in encouraging academic and industrial interaction, and in prom- ing collaborative research and developmental activities in pattern recognition, - chine intelligence and other allied fields, involving scientists, engineers, professionals, researchers and students from India and abroad. The conference is scheduled to be held every alternate year making it an ideal platform for sharing views and expe- ences in these fields in a regular manner. The focus of PReMI 2009 was soft-computing, machine learning, pattern recognition and their applications to diverse fields. As part of PReMI 2009 we had two special workshops. One workshop focused on text mining. The other workshop show-cased industrial and developmental projects in the relevant areas. Premi 2009 attracted 221 submissions from different countries across the world.

These proceedings contain the refereed papers and posters presented at the ?rst Information Retrieval Facility Conference (IRFC), which was held in Vienna on 31 May 2010. The conference provides a multi-disciplinary, scienti?c forum that aims to bring young researchers into contact with industry at an early stage. IRFC 2010 received 20 high-quality submissions, of which 11 were accepted and appear here. The decision whether a paper was presented orally or as poster was solely based on what we thought was the most suitable form of communi- tion, considering we had only a single day for the event. In particular, the form of presentation bears no relation to the quality of the accepted papers, all of which were thoroughly peer reviewed and had to be endorsed by at least three independent reviewers. The Information Retrieval Facility (IRF) is an open IR research institution, managedby a scienti?c board drawnfrom a panel of internationalexperts in the ?eldwhoseroleistopromotethehighestqualityintheresearchsupportedbythe facility. As a non-pro?t research institution, the IRF provides services to IR s- ence in the form of a reference laboratory,hardwareand softwareinfrastructure. Committed to Open Science concepts, the IRF promotes publication of recent scienti?c results and newly developed methods, both in traditional paper form and as data sets freely available to IRF members. Such transparency ensures objective evaluation and comparabilityof results and consequently diversity and sustainability of their further development.

This volume contains selected papers presented at ICFCA 2010, the 8th Int- national Conference on Formal Concept Analysis. The ICFCA conference series aims to be the prime forum for dissemination of advances in applied lattice and order theory, and in particular advances in theory and applications of Formal Concept Analysis. Formal Concept Analysis (FCA) is a ?eld of applied mathematics with its mathematical root in order theory, in particular the theory of complete lattices. Researchershadlongbeenawareofthefactthatthese?eldshavemanypotential applications.FCAemergedinthe1980sfrome?ortstorestructurelattice theory to promote better communication between lattice theorists and potential users of lattice theory. The key theme was the mathematical formalization of c- cept and conceptual hierarchy. Since then, the ?eld has developed into a growing research area in its own right with a thriving theoretical community and an - creasingnumberofapplicationsindataandknowledgeprocessingincludingdata visualization, information retrieval, machine learning, sofware engineering, data analysis, data mining in Web 2.0, analysis of social networks, concept graphs, contextual logic and description logics. ICFCA 2010 took place during March 15-18, 2010 in Agadir, Morocco. We received 37 high-quality submissions out of which 17 were chosen as regular papers in these proceedings after a competitive selection process. Less mature works that were still considered valuable for discussion at the conference were collected in the supplementary proceedings. The papers in the present volume coveradvancesinvariousaspectsofFCArangingfromitstheoreticalfoundations to its applications in numerous other ?elds. In addition to the regular papers, thisvolumealsocontainsfourkeynotepapersarisingfromtheseveninvitedtalks given at the conference. We are also delighted to include a reprint of Bernhard Ganter'sseminalpaper on hiswell-knownalgorithmfor enumerating closedsets.