Download or read book Selected Contributions in Data Analysis and Classification written by Paula Brito and published by Springer Science & Business Media. This book was released on 2007-08-27 with total page 619 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents recent methodological developments in data analysis and classification. It covers a wide range of topics, including methods for classification and clustering, dissimilarity analysis, consensus methods, conceptual analysis of data, and data mining and knowledge discovery in databases. The book also presents a wide variety of applications, in fields such as biology, micro-array analysis, cyber traffic, and bank fraud detection.

Download or read book Contribution m thodologique la fouille de donn es complexes written by Jérémie Clech and published by . This book was released on 2004 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: Au cours de cette thèse, nous abordons la problématique de l'extraction de connaissances à partir de données complexes. Notre motivation est issue de l'accroissement du besoin de traiter de telles données, du principalement à l'explosion des technologies de l'information véhiculant une forte diffusion de documents complexes. La fouille de données complexes se propose de fournir un modèle d'analyse permettant d'intégrer de larges variétés de données, structurées ou non, locales ou distantes. Le point de vue retenu est de dire que face à une tâche d'extraction des connaissances, l'utilisateur doit être libéré des contraintes liées à l'organisation, le codage, le format, la représentation des données. Il doit accéder au contenu. Nous reprenons les étapes du processus d'extraction de connaissances afin de traiter dans un cadre général ces données fortement hétérogènes. L'aboutissement du processus étant l'exploitation de ces données, nous proposons ici un environnement d'exploration visuelle reposant à la fois sur une représentation globale du corpus, sur une contextualisation d'un individu particulier et sur la visualisation à proprement parlée des documents. En outre, nous adaptons l'architecture des systèmes de recherch d'information à ce type de données. Nous avons proposé un système de recherche basé sur l'exploitation de la contextualisation d'un document et un autre sur un processus de fouille de données dans le but de prendre en compte la perception de l'utilisateur vis à vis de la requête posée en fonction de son jugement face aux documents retournés par le système. Enfin, nous décrivons des applications concrètes liées à l'exploitation de données complexes.

Download or read book Data Mining in Large Sets of Complex Data written by Robson Leonardo Ferreira Cordeiro and published by Springer Science & Business Media. This book was released on 2013-01-11 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: The amount and the complexity of the data gathered by current enterprises are increasing at an exponential rate. Consequently, the analysis of Big Data is nowadays a central challenge in Computer Science, especially for complex data. For example, given a satellite image database containing tens of Terabytes, how can we find regions aiming at identifying native rainforests, deforestation or reforestation? Can it be made automatically? Based on the work discussed in this book, the answers to both questions are a sound “yes”, and the results can be obtained in just minutes. In fact, results that used to require days or weeks of hard work from human specialists can now be obtained in minutes with high precision. Data Mining in Large Sets of Complex Data discusses new algorithms that take steps forward from traditional data mining (especially for clustering) by considering large, complex datasets. Usually, other works focus in one aspect, either data size or complexity. This work considers both: it enables mining complex data from high impact applications, such as breast cancer diagnosis, region classification in satellite images, assistance to climate change forecast, recommendation systems for the Web and social networks; the data are large in the Terabyte-scale, not in Giga as usual; and very accurate results are found in just minutes. Thus, it provides a crucial and well timed contribution for allowing the creation of real time applications that deal with Big Data of high complexity in which mining on the fly can make an immeasurable difference, such as supporting cancer diagnosis or detecting deforestation.

Download or read book Data Mining for Business Applications written by Longbing Cao and published by Springer Science & Business Media. This book was released on 2008-10-03 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from “data-centered pattern mining” to “domain driven actionable knowledge discovery” for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business.

Download or read book Contribution la fouille de donn es written by Olivier Couturier and published by . This book was released on 2005 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: Au regard du nombre croissant des grandes bases de données, déterminer la façon dont sont organisées les données, les interpréter et en extraire des informations utiles est un problème difficile et ouvert. En effet, à l'heure actuelle, notre capacité à collecter et stocker les données de tout type, outrepasse nos possibilités d'analyse, de synthèse et d'extraction de connaissances dans les données. Notre travail se situe au niveau de la recherche de règles d'association qui constitue une tâche de fouille de données. Cette dernière présente des résultats qui permettent aux experts de facilement interpréter les règles une à une. Les méthodes de génération sont combinatoires et engendrent un nombre élevé de règles qui sont difficilement exploitables. Plusieurs approches de réduction de ce nombre ont été proposées comme l'usage de mesures de qualité, le filtrage syntaxique par contraintes, la compression par les bases représentatives ou génériques. Cependant, ces approches n'intègrent pas l'expert dans le déroulement du processus limitant ainsi l'aspect interactif du processus. En effet, l'expert ne sait pas toujours initialement quelle connaissance il souhaite obtenir. Nous analysons l'activité cognitive de l'expert dans différents processus de recherche de règles d'association et nous montrons que dans ces approches, l'expert n'intervient pas durant les tâches du processus. Pour accroître cette interactivité avec l'expert, il est nécessaire que celui-ci soit au coeur du processus afin de répondre à l'un des objectifs de l'ECD. Nous nous basons sur les systèmes orientés-tâches, qui se focalisent sur les différentes tâches que l'expert doit réaliser, et proposons l'algorithme SHARK qui est une approche hybride basée sur l'utilisation d'une recherche hiérarchique s'appuyant sur une taxinomie d'attributs et d'une approche anthropocentrée de manière à inclure l'expert dans le processus. Nous couplons ainsi la connaissance explicite fournie par l'algorithme et la connaissance tacite de l'expert. L'utilisation d'une interface graphique adaptée s'avère donc nécessaire pour que l'expert puisse interagir de manière optimale avec le processus. L'efficacité de cet algorithme a été montrée sur un problème réel de marketing faisant intervenir des experts du monde bancaire. En outre, la fouille de données visuelle présente un intérêt non négligeable puisque l'esprit humain peut traiter une plus grande quantité d'informations de manière visuelle. Comme des quantités très importantes de règles sont générées, la fouille de données visuelle s'avère être une étape incontournable pour améliorer encore notre approche. Nous présentons un état de l'art des principales techniques de visualisation de règles d'association. Parmi ces représentations, nous nous focalisons sur les représentations de type matrice 3D présentant la particularité de générer des occlusions. Une occlusion est un chevauchement d'objets dans un environnement 3D rendant certains de ces objets pas ou peu visibles. Après avoir défini formellement le problème d'occlusions, nous montrons qu'il s'agit d'un problème d'optimisation qui est de trouver le meilleur ordre possible des itemsets sur les deux axes pour limiter les occlusions. Nous proposons une heuristique permettant de réduire significativement les occlusions générées. Les résultats que nous avons obtenus sont présentés et discutés.

Download or read book Scientific Data Mining and Knowledge Discovery written by Mohamed Medhat Gaber and published by Springer Science & Business Media. This book was released on 2009-09-19 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mohamed Medhat Gaber “It is not my aim to surprise or shock you – but the simplest way I can summarise is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until – in a visible future – the range of problems they can handle will be coextensive with the range to which the human mind has been applied” by Herbert A. Simon (1916-2001) 1Overview This book suits both graduate students and researchers with a focus on discovering knowledge from scienti c data. The use of computational power for data analysis and knowledge discovery in scienti c disciplines has found its roots with the re- lution of high-performance computing systems. Computational science in physics, chemistry, and biology represents the rst step towards automation of data analysis tasks. The rational behind the developmentof computationalscience in different - eas was automating mathematical operations performed in those areas. There was no attention paid to the scienti c discovery process. Automated Scienti c Disc- ery (ASD) [1–3] represents the second natural step. ASD attempted to automate the process of theory discovery supported by studies in philosophy of science and cognitive sciences. Although early research articles have shown great successes, the area has not evolved due to many reasons. The most important reason was the lack of interaction between scientists and the automating systems.

Download or read book Data Mining written by Robert Stahlbock and published by Springer Science & Business Media. This book was released on 2009-11-10 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the course of the last twenty years, research in data mining has seen a substantial increase in interest, attracting original contributions from various disciplines including computer science, statistics, operations research, and information systems. Data mining supports a wide range of applications, from medical decision making, bioinformatics, web-usage mining, and text and image recognition to prominent business applications in corporate planning, direct marketing, and credit scoring. Research in information systems equally reflects this inter- and multidisciplinary approach, thereby advocating a series of papers at the intersection of data mining and information systems research. This special issue of Annals of Information Systems contains original papers and substantial extensions of selected papers from the 2007 and 2008 International Conference on Data Mining (DMIN’07 and DMIN’08, Las Vegas, NV) that have been rigorously peer-reviewed. The issue brings together topics on both information systems and data mining, and aims to give the reader a current snapshot of the contemporary research and state of the art practice in data mining.

Download or read book Post Mine Repeat written by Helen Kennedy and published by Springer. This book was released on 2016-05-14 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, Helen Kennedy argues that as social media data mining becomes more and more ordinary, as we post, mine and repeat, new data relations emerge. These new data relations are characterised by a widespread desire for numbers and the troubling consequences of this desire, and also by the possibility of doing good with data and resisting data power, by new and old concerns, and by instability and contradiction. Drawing on action research with public sector organisations, interviews with commercial social insights companies and their clients, focus groups with social media users and other research, Kennedy provides a fascinating and detailed account of living with social media data mining inside the organisations that make up the fabric of everyday life.

Download or read book Transparent Data Mining for Big and Small Data written by Tania Cerquitelli and published by Springer. This book was released on 2017-05-09 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent solutions are tailored to different domain experts, and experimental metrics for evaluating algorithmic transparency are presented. The book also discusses societal effects of black box vs. transparent approaches to data mining, as well as real-world use cases for these approaches.As algorithms increasingly support different aspects of modern life, a greater level of transparency is sorely needed, not least because discrimination and biases have to be avoided. With contributions from domain experts, this book provides an overview of an emerging area of data mining that has profound societal consequences, and provides the technical background to for readers to contribute to the field or to put existing approaches to practical use.

Download or read book Data Mining and Knowledge Discovery for Big Data written by Wesley W. Chu and published by Springer. This book was released on 2013-10-09 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation. The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.

Download or read book 5th International Symposium on Data Mining Applications written by Mamdouh Alenezi and published by Springer. This book was released on 2018-03-28 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 5th Symposium on Data Mining Applications (SDMA 2018) provides valuable opportunities for technical collaboration among data mining and machine learning researchers in Saudi Arabia, Gulf Cooperation Council (GCC) countries and the Middle East region. This book gathers the proceedings of the SDMA 2018. All papers were peer-reviewed based on a strict policy concerning the originality, significance to the area, scientific vigor and quality of the contribution, and address the following research areas.• Applications: Applications of data mining in domains including databases, social networks, web, bioinformatics, finance, healthcare, and security.• Algorithms: Data mining and machine learning foundations, algorithms, models, and theory.• Text Mining: Semantic analysis and mining text in Arabic, semi-structured, streaming, multimedia data.• Framework: Data mining frameworks, platforms and systems implementation.• Visualizations: Data visualization and modeling.

Download or read book Contemporary Experimental Design Multivariate Analysis and Data Mining written by Jianqing Fan and published by Springer Nature. This book was released on 2020-05-22 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: The collection and analysis of data play an important role in many fields of science and technology, such as computational biology, quantitative finance, information engineering, machine learning, neuroscience, medicine, and the social sciences. Especially in the era of big data, researchers can easily collect data characterised by massive dimensions and complexity. In celebration of Professor Kai-Tai Fang’s 80th birthday, we present this book, which furthers new and exciting developments in modern statistical theories, methods and applications. The book features four review papers on Professor Fang’s numerous contributions to the fields of experimental design, multivariate analysis, data mining and education. It also contains twenty research articles contributed by prominent and active figures in their fields. The articles cover a wide range of important topics such as experimental design, multivariate analysis, data mining, hypothesis testing and statistical models.

Download or read book Principles of Data Mining written by David J. Hand and published by MIT Press. This book was released on 2001-08-17 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

Download or read book Classification and Data Analysis written by Krzysztof Jajuga and published by Springer Nature. This book was released on 2020-08-28 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume gathers peer-reviewed contributions on data analysis, classification and related areas presented at the 28th Conference of the Section on Classification and Data Analysis of the Polish Statistical Association, SKAD 2019, held in Szczecin, Poland, on September 18–20, 2019. Providing a balance between theoretical and methodological contributions and empirical papers, it covers a broad variety of topics, ranging from multivariate data analysis, classification and regression, symbolic (and other) data analysis, visualization, data mining, and computer methods to composite measures, and numerous applications of data analysis methods in economics, finance and other social sciences. The book is intended for a wide audience, including researchers at universities and research institutions, graduate and doctoral students, practitioners, data scientists and employees in public statistical institutions.

Download or read book Design and Implementation of Data Mining Tools written by Bhavani Thuraisingham and published by Auerbach Publications. This book was released on 2009-06-18 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on three applications of data mining, Design and Implementation of Data Mining Tools explains how to create and employ systems and tools for intrusion detection, Web page surfing prediction, and image classification. Mainly based on the authors’ own research work, the book takes a practical approach to the subject. The first part of the book reviews data mining techniques, such as artificial neural networks and support vector machines, as well as data mining applications. The second section covers the design and implementation of data mining tools for intrusion detection. It examines various designs and performance results, along with the strengths and weaknesses of the approaches. The third part presents techniques to solve the WWW prediction problem. The final part describes models that the authors have developed for image classification. Showing step by step how data mining tools are developed, this hands-on guide discusses the performance results, limitations, and unique contributions of data mining systems. It provides essential information for technologists to decide on the tools to select for a particular application, for developers to focus on alternative designs if an approach is unsuitable, and for managers to choose whether to proceed with a data mining project.

Download or read book Data Mining written by Jiawei Han and published by Morgan Kaufmann. This book was released on 2022-07-02 with total page 786 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets. After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining. - Presents a comprehensive new chapter on deep learning, including improving training of deep learning models, convolutional neural networks, recurrent neural networks, and graph neural networks - Addresses advanced topics in one dedicated chapter: data mining trends and research frontiers, including mining rich data types (text, spatiotemporal data, and graph/networks), data mining applications (such as sentiment analysis, truth discovery, and information propagattion), data mining methodologie and systems, and data mining and society - Provides a comprehensive, practical look at the concepts and techniques needed to get the most out of your data - Visit the author-hosted companion site, https://hanj.cs.illinois.edu/bk4/ for downloadable lecture slides and errata

Download or read book Data Mining written by Charu C. Aggarwal and published by Springer. This book was released on 2015-04-13 with total page 746 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago