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Author: Evangelos Triantaphyllou
Publisher: Springer Science & Business Media
ISBN: 144191630X
Category : Computers
Languages : en
Pages : 371
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Book Description
The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem studied by data mining and knowledge discovery, and it seems that a very popular one is formulated in terms of binary attributes. In this setting, states of nature of the application area under consideration are described by Boolean vectors de ned on some attributes. 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. The main problem in DM&KD is de ned as nding rules for recognizing (cl- sifying) new data points of unknown class, i. e. , deciding which of them are positive and which are negative. 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.
Author: Evangelos Triantaphyllou
Publisher: Springer Science & Business Media
ISBN: 144191630X
Category : Computers
Languages : en
Pages : 371
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Book Description
The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem studied by data mining and knowledge discovery, and it seems that a very popular one is formulated in terms of binary attributes. In this setting, states of nature of the application area under consideration are described by Boolean vectors de ned on some attributes. 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. The main problem in DM&KD is de ned as nding rules for recognizing (cl- sifying) new data points of unknown class, i. e. , deciding which of them are positive and which are negative. 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.
Author: Jan Zytkow
Publisher: Springer Science & Business Media
ISBN: 3540664904
Category : Computers
Languages : en
Pages : 608
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Book Description
This book constitutes the refereed proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD'99, held in Prague, Czech Republic in September 1999. The 28 revised full papers and 48 poster presentations were carefully reviewed and selected from 106 full papers submitted. The papers are organized in topical sections on time series, applications, taxonomies and partitions, logic methods, distributed and multirelational databases, text mining and feature selection, rules and induction, and interesting and unusual issues.
Author: Oded Maimon
Publisher: Springer Science & Business Media
ISBN: 038725465X
Category : Computers
Languages : en
Pages : 1378
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Book Description
Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
Author: Evangelos Triantaphyllou
Publisher: Springer Science & Business Media
ISBN: 0387342966
Category : Computers
Languages : en
Pages : 784
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Book Description
This book outlines the core theory and practice of data mining and knowledge discovery (DM & KD) examining theoretical foundations for various methods, and presenting an array of examples, many drawn from real-life applications. Most theoretical developments are accompanied by extensive empirical analysis, offering a deep insight into both theoretical and practical aspects of the subject. The book presents the combined research experiences of 40 expert contributors of world renown.
Author: Lutz H. Hamel
Publisher: John Wiley & Sons
ISBN: 1118211030
Category : Computers
Languages : en
Pages : 211
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Book Description
An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-class classification Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.
Author: Tsutomu Sasao
Publisher: Springer Nature
ISBN: 3031353471
Category : Technology & Engineering
Languages : en
Pages : 148
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Book Description
This book introduces a novel perspective on machine learning, offering distinct advantages over neural network-based techniques. This approach boasts a reduced hardware requirement, lower power consumption, and enhanced interpretability. The applications of this approach encompass high-speed classifications, including packet classification, network intrusion detection, and exotic particle detection in high-energy physics. Moreover, it finds utility in medical diagnosis scenarios characterized by small training sets and imbalanced data. The resulting rule generated by this method can be implemented either in software or hardware. In the case of hardware implementation, circuit design can employ look-up tables (memory), rather than threshold gates. The methodology described in this book involves extracting a set of rules from a training set, composed of categorical variable vectors and their corresponding classes. Unnecessary variables are eliminated, and the rules are simplified before being transformed into a sum-of-products (SOP) form. The resulting SOP exhibits the ability to generalize and predict outputs for new inputs. The effectiveness of this approach is demonstrated through numerous examples and experimental results using the University of California-Irvine (UCI) dataset. This book is primarily intended for graduate students and researchers in the fields of logic synthesis, machine learning, and data mining. It assumes a foundational understanding of logic synthesis, while familiarity with linear algebra and statistics would be beneficial for readers.
Author: Pablo Moscato
Publisher: Springer
ISBN: 3030062228
Category : Computers
Languages : en
Pages : 1000
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Book Description
This two-volume handbook presents a collection of novel methodologies with applications and illustrative examples in the areas of data-driven computational social sciences. Throughout this handbook, the focus is kept specifically on business and consumer-oriented applications with interesting sections ranging from clustering and network analysis, meta-analytics, memetic algorithms, machine learning, recommender systems methodologies, parallel pattern mining and data mining to specific applications in market segmentation, travel, fashion or entertainment analytics. A must-read for anyone in data-analytics, marketing, behavior modelling and computational social science, interested in the latest applications of new computer science methodologies. The chapters are contributed by leading experts in the associated fields.The chapters cover technical aspects at different levels, some of which are introductory and could be used for teaching. Some chapters aim at building a common understanding of the methodologies and recent application areas including the introduction of new theoretical results in the complexity of core problems. Business and marketing professionals may use the book to familiarize themselves with some important foundations of data science. The work is a good starting point to establish an open dialogue of communication between professionals and researchers from different fields. Together, the two volumes present a number of different new directions in Business and Customer Analytics with an emphasis in personalization of services, the development of new mathematical models and new algorithms, heuristics and metaheuristics applied to the challenging problems in the field. Sections of the book have introductory material to more specific and advanced themes in some of the chapters, allowing the volumes to be used as an advanced textbook. Clustering, Proximity Graphs, Pattern Mining, Frequent Itemset Mining, Feature Engineering, Network and Community Detection, Network-based Recommending Systems and Visualization, are some of the topics in the first volume. Techniques on Memetic Algorithms and their applications to Business Analytics and Data Science are surveyed in the second volume; applications in Team Orienteering, Competitive Facility-location, and Visualization of Products and Consumers are also discussed. The second volume also includes an introduction to Meta-Analytics, and to the application areas of Fashion and Travel Analytics. Overall, the two-volume set helps to describe some fundamentals, acts as a bridge between different disciplines, and presents important results in a rapidly moving field combining powerful optimization techniques allied to new mathematical models critical for personalization of services. Academics and professionals working in the area of business anyalytics, data science, operations research and marketing will find this handbook valuable as a reference. Students studying these fields will find this handbook useful and helpful as a secondary textbook.
Author: Naidenova, Xenia
Publisher: IGI Global
ISBN: 1605668117
Category : Computers
Languages : en
Pages : 424
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Book Description
This book suggests that classification is a key to human commonsense reasoning and transforms traditional considerations of data and knowledge communications, presenting an effective classification of logical rules used in the modeling of commonsense reasoning.
Author: Juri Yanase
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 51
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Book Description
Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort expended in the interface of medicine and computer science. As some CAD systems in medicine try to emulate the diagnostic decision-making process of medical experts, they can be considered as expert systems in medicine.
Author: Damir Gainanov
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110480301
Category : Mathematics
Languages : en
Pages : 182
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Book Description
This monograph deals with mathematical constructions that are foundational in such an important area of data mining as pattern recognition. By using combinatorial and graph theoretic techniques, a closer look is taken at infeasible systems of linear inequalities, whose generalized solutions act as building blocks of geometric decision rules for pattern recognition. Infeasible systems of linear inequalities prove to be a key object in pattern recognition problems described in geometric terms thanks to the committee method. Such infeasible systems of inequalities represent an important special subclass of infeasible systems of constraints with a monotonicity property – systems whose multi-indices of feasible subsystems form abstract simplicial complexes (independence systems), which are fundamental objects of combinatorial topology. The methods of data mining and machine learning discussed in this monograph form the foundation of technologies like big data and deep learning, which play a growing role in many areas of human-technology interaction and help to find solutions, better solutions and excellent solutions. Contents: Preface Pattern recognition, infeasible systems of linear inequalities, and graphs Infeasible monotone systems of constraints Complexes, (hyper)graphs, and inequality systems Polytopes, positive bases, and inequality systems Monotone Boolean functions, complexes, graphs, and inequality systems Inequality systems, committees, (hyper)graphs, and alternative covers Bibliography List of notation Index
