Knowledge Discovery from Multi-Sourced Data

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Publisher : Springer Nature
ISBN 13 : 9811918791
Total Pages : 91 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Knowledge Discovery from Multi-Sourced Data by : Chen Ye

Download or read book Knowledge Discovery from Multi-Sourced Data written by Chen Ye and published by Springer Nature. This book was released on 2022-06-13 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses several knowledge discovery problems on multi-sourced data where the theories, techniques, and methods in data cleaning, data mining, and natural language processing are synthetically used. This book mainly focuses on three data models: the multi-sourced isomorphic data, the multi-sourced heterogeneous data, and the text data. On the basis of three data models, this book studies the knowledge discovery problems including truth discovery and fact discovery on multi-sourced data from four important properties: relevance, inconsistency, sparseness, and heterogeneity, which is useful for specialists as well as graduate students. Data, even describing the same object or event, can come from a variety of sources such as crowd workers and social media users. However, noisy pieces of data or information are unavoidable. Facing the daunting scale of data, it is unrealistic to expect humans to “label” or tell which data source is more reliable. Hence, it is crucial to identify trustworthy information from multiple noisy information sources, referring to the task of knowledge discovery. At present, the knowledge discovery research for multi-sourced data mainly faces two challenges. On the structural level, it is essential to consider the different characteristics of data composition and application scenarios and define the knowledge discovery problem on different occasions. On the algorithm level, the knowledge discovery task needs to consider different levels of information conflicts and design efficient algorithms to mine more valuable information using multiple clues. Existing knowledge discovery methods have defects on both the structural level and the algorithm level, making the knowledge discovery problem far from totally solved.

Knowledge Discovery from Data Streams

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Author :
Publisher : CRC Press
ISBN 13 : 1439826129
Total Pages : 256 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Knowledge Discovery from Data Streams by : Joao Gama

Download or read book Knowledge Discovery from Data Streams written by Joao Gama and published by CRC Press. This book was released on 2010-05-25 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents

Knowledge Discovery in Multiple Databases

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Publisher : Springer Science & Business Media
ISBN 13 : 0857293885
Total Pages : 237 pages
Book Rating : 4.8/5 (572 download)

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Book Synopsis Knowledge Discovery in Multiple Databases by : Shichao Zhang

Download or read book Knowledge Discovery in Multiple Databases written by Shichao Zhang and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining.

Advances in Knowledge Discovery and Data Mining

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Publisher : Springer Nature
ISBN 13 : 3030474267
Total Pages : 906 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Advances in Knowledge Discovery and Data Mining by : Hady W. Lauw

Download or read book Advances in Knowledge Discovery and Data Mining written by Hady W. Lauw and published by Springer Nature. This book was released on 2020-05-08 with total page 906 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNAI 12084 and 12085 constitutes the thoroughly refereed proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, which was due to be held in Singapore, in May 2020. The conference was held virtually due to the COVID-19 pandemic. The 135 full papers presented were carefully reviewed and selected from 628 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: recommender systems; classification; clustering; mining social networks; representation learning and embedding; mining behavioral data; deep learning; feature extraction and selection; human, domain, organizational and social factors in data mining; mining sequential data; mining imbalanced data; association; privacy and security; supervised learning; novel algorithms; mining multi-media/multi-dimensional data; application; mining graph and network data; anomaly detection and analytics; mining spatial, temporal, unstructured and semi-structured data; sentiment analysis; statistical/graphical model; multi-source/distributed/parallel/cloud computing.

Advances in Knowledge Discovery and Data Mining

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Publisher : Springer Nature
ISBN 13 : 3030474364
Total Pages : 936 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Advances in Knowledge Discovery and Data Mining by : Hady W. Lauw

Download or read book Advances in Knowledge Discovery and Data Mining written by Hady W. Lauw and published by Springer Nature. This book was released on 2020-05-08 with total page 936 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNAI 12084 and 12085 constitutes the thoroughly refereed proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, which was due to be held in Singapore, in May 2020. The conference was held virtually due to the COVID-19 pandemic. The 135 full papers presented were carefully reviewed and selected from 628 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: recommender systems; classification; clustering; mining social networks; representation learning and embedding; mining behavioral data; deep learning; feature extraction and selection; human, domain, organizational and social factors in data mining; mining sequential data; mining imbalanced data; association; privacy and security; supervised learning; novel algorithms; mining multi-media/multi-dimensional data; application; mining graph and network data; anomaly detection and analytics; mining spatial, temporal, unstructured and semi-structured data; sentiment analysis; statistical/graphical model; multi-source/distributed/parallel/cloud computing.

Soft Computing for Knowledge Discovery and Data Mining

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Publisher : Springer Science & Business Media
ISBN 13 : 038769935X
Total Pages : 431 pages
Book Rating : 4.3/5 (876 download)

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Book Synopsis Soft Computing for Knowledge Discovery and Data Mining by : Oded Maimon

Download or read book Soft Computing for Knowledge Discovery and Data Mining written by Oded Maimon and published by Springer Science & Business Media. This book was released on 2007-10-25 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. This book introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining and includes various real-world case studies with detailed results.

Knowledge Discovery in Data with Selected Java Open Source Software

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Publisher : Grin Publishing
ISBN 13 : 9783668443112
Total Pages : 20 pages
Book Rating : 4.4/5 (431 download)

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Book Synopsis Knowledge Discovery in Data with Selected Java Open Source Software by : Et Al

Download or read book Knowledge Discovery in Data with Selected Java Open Source Software written by Et Al and published by Grin Publishing. This book was released on 2017-06-14 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research Paper (postgraduate) from the year 2008 in the subject Computer Science - Applied, grade: 4.0, University of Louisville (Speed College of Engineering), language: English, abstract: We give an overview of our experience in utilizing several open source packages and composing them into sophisticated applications to solve several challenging problems as part of some of the research projects at the Knowledge Discovery & Web Mining lab at the Universe of Louisville. The projects have a common theme of knowledge discovery, however their application domains span a variety of areas. These areas range from mining Web data streams to mining Astronomy related image data, as well as Web information retrieval in social multimedia websites and e-learning platforms. As is already known, a significant proportion of the effort in any real life project involving knowledge discovery in data (KDD) is devoted to the early and final stages of KDD, i.e., the data collection and preprocessing, and the visualization of the results. Given the nature of the data in our projects, we expose our experience in handling text data and image data as part of the KDD process. In addition to the open source packages that we used, we will briefly present some of the stand-alone software that we developed in the lab, in particular a suite of software for clustering and for stream data mining.

Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

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Publisher : Springer
ISBN 13 : 3662439689
Total Pages : 373 pages
Book Rating : 4.6/5 (624 download)

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Book Synopsis Interactive Knowledge Discovery and Data Mining in Biomedical Informatics by : Andreas Holzinger

Download or read book Interactive Knowledge Discovery and Data Mining in Biomedical Informatics written by Andreas Holzinger and published by Springer. This book was released on 2014-06-17 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of = 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning./ppThis state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.

Advances in Knowledge Discovery in Databases

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Publisher : Springer
ISBN 13 : 9783319366067
Total Pages : 0 pages
Book Rating : 4.3/5 (66 download)

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Book Synopsis Advances in Knowledge Discovery in Databases by : Animesh Adhikari

Download or read book Advances in Knowledge Discovery in Databases written by Animesh Adhikari and published by Springer. This book was released on 2016-09-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances in Knowledge discovery in databases (KDD) with a focus on the areas of market basket database, time-stamped databases and multiple related databases. Various interesting and intelligent algorithms are reported on data mining tasks. A large number of association measures are presented, which play significant roles in decision support applications. This book presents, discusses and contrasts new developments in mining time-stamped data, time-based data analyses, the identification of temporal patterns, the mining of multiple related databases, as well as local patterns analysis.

Advances in Knowledge Discovery and Data Mining

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Publisher :
ISBN 13 :
Total Pages : 638 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Advances in Knowledge Discovery and Data Mining by : Usama M. Fayyad

Download or read book Advances in Knowledge Discovery and Data Mining written by Usama M. Fayyad and published by . This book was released on 1996 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Eight sections of this book span fundamental issues of knowledge discovery, classification and clustering, trend and deviation analysis, dependency derivation, integrated discovery systems, augumented database systems and application case studies. The appendices provide a list of terms used in the literature of the field of data mining and knowledge discovery in databases, and a list of online resources for the KDD researcher.

Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases

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Author :
Publisher : Springer
ISBN 13 : 354077467X
Total Pages : 169 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases by : Ashish Ghosh

Download or read book Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases written by Ashish Ghosh and published by Springer. This book was released on 2008-02-28 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.

Advances in Knowledge Discovery and Data Mining

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Publisher : Springer
ISBN 13 : 3319930400
Total Pages : 852 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Advances in Knowledge Discovery and Data Mining by : Dinh Phung

Download or read book Advances in Knowledge Discovery and Data Mining written by Dinh Phung and published by Springer. This book was released on 2018-06-16 with total page 852 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set, LNAI 10937, 10938, and 10939, constitutes the thoroughly refereed proceedings of the 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018, held in Melbourne, VIC, Australia, in June 2018. The 164 full papers were carefully reviewed and selected from 592 submissions. The volumes present papers focusing on new ideas, original research results and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems and the emerging applications.

Advanced Methods for Knowledge Discovery from Complex Data

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Publisher : Springer Science & Business Media
ISBN 13 : 1846282845
Total Pages : 375 pages
Book Rating : 4.8/5 (462 download)

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Book Synopsis Advanced Methods for Knowledge Discovery from Complex Data by : Ujjwal Maulik

Download or read book Advanced Methods for Knowledge Discovery from Complex Data written by Ujjwal Maulik and published by Springer Science & Business Media. This book was released on 2006-05-06 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters.

Knowledge Discovery and Data Mining

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Publisher : Springer Science & Business Media
ISBN 13 : 9780792366478
Total Pages : 192 pages
Book Rating : 4.3/5 (664 download)

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Book Synopsis Knowledge Discovery and Data Mining by : O. Maimon

Download or read book Knowledge Discovery and Data Mining written by O. Maimon and published by Springer Science & Business Media. This book was released on 2000-12-31 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a specific and unified approach to Knowledge Discovery and Data Mining, termed IFN for Information Fuzzy Network methodology. Data Mining (DM) is the science of modelling and generalizing common patterns from large sets of multi-type data. DM is a part of KDD, which is the overall process for Knowledge Discovery in Databases. The accessibility and abundance of information today makes this a topic of particular importance and need. The book has three main parts complemented by appendices as well as software and project data that are accessible from the book's web site (http://www.eng.tau.ac.iV-maimonlifn-kdg£). Part I (Chapters 1-4) starts with the topic of KDD and DM in general and makes reference to other works in the field, especially those related to the information theoretic approach. The remainder of the book presents our work, starting with the IFN theory and algorithms. Part II (Chapters 5-6) discusses the methodology of application and includes case studies. Then in Part III (Chapters 7-9) a comparative study is presented, concluding with some advanced methods and open problems. The IFN, being a generic methodology, applies to a variety of fields, such as manufacturing, finance, health care, medicine, insurance, and human resources. The appendices expand on the relevant theoretical background and present descriptions of sample projects (including detailed results).

Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications

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Publisher : IGI Global
ISBN 13 : 1605667498
Total Pages : 426 pages
Book Rating : 4.6/5 (56 download)

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Book Synopsis Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications by : Nguyen, Tho Manh

Download or read book Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications written by Nguyen, Tho Manh and published by IGI Global. This book was released on 2009-07-31 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, researchers have focused on challenging problems facing the development of data warehousing, knowledge discovery, and data mining applications.

Mathematical Methods for Knowledge Discovery and Data Mining

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Publisher : IGI Global
ISBN 13 :
Total Pages : 402 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis Mathematical Methods for Knowledge Discovery and Data Mining by : Giovanni Felici

Download or read book Mathematical Methods for Knowledge Discovery and Data Mining written by Giovanni Felici and published by IGI Global. This book was released on 2008 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation The field of data mining has seen a demand in recent years for the development of ideas and results in an integrated structure. Mathematical Methods for Knowledge Discovery & Data Mining focuses on the mathematical models and methods that support most data mining applications and solution techniques, covering such topics as association rules; Bayesian methods; data visualization; kernel methods; neural networks; text, speech, and image recognition; and many others. This Premier Reference Source is an invaluable resource for scholars and practitioners in the fields of biomedicine, engineering, finance and insurance, manufacturing, marketing, performance measurement, and telecommunications.

Advances in Knowledge Discovery and Data Mining

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Publisher : Springer Nature
ISBN 13 : 9819722624
Total Pages : 431 pages
Book Rating : 4.8/5 (197 download)

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Book Synopsis Advances in Knowledge Discovery and Data Mining by : De-Nian Yang

Download or read book Advances in Knowledge Discovery and Data Mining written by De-Nian Yang and published by Springer Nature. This book was released on with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: