Download or read book Data Mining A Heuristic Approach written by Abbass, Hussein A. and published by IGI Global. This book was released on 2001-07-01 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Real life problems are known to be messy, dynamic and multi-objective, and involve high levels of uncertainty and constraints. Because traditional problem-solving methods are no longer capable of handling this level of complexity, heuristic search methods have attracted increasing attention in recent years for solving such problems. Inspired by nature, biology, statistical mechanics, physics and neuroscience, heuristics techniques are used to solve many problems where traditional methods have failed. Data Mining: A Heuristic Approach will be a repository for the applications of these techniques in the area of data mining.
Download or read book Heuristic Data Sampling Approach for Data Mining Preparation Under Big Data Environment written by 彭懷德 and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Heuristics in Analytics written by Carlos Andre Reis Pinheiro and published by John Wiley & Sons. This book was released on 2014-01-31 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Employ heuristic adjustments for truly accurate analysis Heuristics in Analytics presents an approach to analysis that accounts for the randomness of business and the competitive marketplace, creating a model that more accurately reflects the scenario at hand. With an emphasis on the importance of proper analytical tools, the book describes the analytical process from exploratory analysis through model developments, to deployments and possible outcomes. Beginning with an introduction to heuristic concepts, readers will find heuristics applied to statistics and probability, mathematics, stochastic, and artificial intelligence models, ending with the knowledge applications that solve business problems. Case studies illustrate the everyday application and implication of the techniques presented, while the heuristic approach is integrated into analytical modeling, graph analysis, text analytics, and more. Robust analytics has become crucial in the corporate environment, and randomness plays an enormous role in business and the competitive marketplace. Failing to account for randomness can steer a model in an entirely wrong direction, negatively affecting the final outcome and potentially devastating the bottom line. Heuristics in Analytics describes how the heuristic characteristics of analysis can be overcome with problem design, math and statistics, helping readers to: Realize just how random the world is, and how unplanned events can affect analysis Integrate heuristic and analytical approaches to modeling and problem solving Discover how graph analysis is applied in real-world scenarios around the globe Apply analytical knowledge to customer behavior, insolvency prevention, fraud detection, and more Understand how text analytics can be applied to increase the business knowledge Every single factor, no matter how large or how small, must be taken into account when modeling a scenario or event—even the unknowns. The presence or absence of even a single detail can dramatically alter eventual outcomes. From raw data to final report, Heuristics in Analytics contains the information analysts need to improve accuracy, and ultimately, predictive, and descriptive power.
Download or read book Data Mining and Constraint Programming written by Christian Bessiere and published by Springer. This book was released on 2016-12-01 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: A successful integration of constraint programming and data mining has the potential to lead to a new ICT paradigm with far reaching implications. It could change the face of data mining and machine learning, as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to identify and update constraints and optimization criteria, but also to employ constraints and criteria in data mining and machine learning in order to discover models compatible with prior knowledge. This book reports on some key results obtained on this integrated and cross- disciplinary approach within the European FP7 FET Open project no. 284715 on “Inductive Constraint Programming” and a number of associated workshops and Dagstuhl seminars. The book is structured in five parts: background; learning to model; learning to solve; constraint programming for data mining; and showcases.
Download or read book Data Mining and Knowledge Discovery via Logic Based Methods written by Evangelos Triantaphyllou and published by Springer Science & Business Media. This book was released on 2010-06-08 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Data Mining written by John Wang and published by IGI Global. This book was released on 2003-01-01 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: "An overview of the multidisciplinary field of data mining, this book focuses specifically on new methodologies and case studies. Included are case studies written by 44 leading scientists and talented young scholars from seven different countries. Topics covered include data mining based on rough sets, the impact of missing data, and mining free text for structure. In addition, the four basic mining operations supported by numerous mining techniques are addressed: predictive model creation supported by supervised induction techniques; link analysis supported by association discovery and sequence discovery techniques; DB segmentation supported by clustering techniques; and deviation detection supported by statistical techniques."
Download or read book Data Mining in Agriculture written by Antonio Mucherino and published by Springer Science & Business Media. This book was released on 2009-08-19 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®.
Download or read book Stream Data Mining Algorithms and Their Probabilistic Properties written by Leszek Rutkowski and published by Springer. This book was released on 2019-03-16 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who deal with stream data, e.g. in telecommunication, banking, and sensor networks.
Download or read book Advances in Data Mining Applications and Theoretical Aspects written by Petra Perner and published by Springer. This book was released on 2016-06-27 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 16th Industrial Conference on Advances in Data Mining, ICDM 2016, held in New York, NY, USA, in July 2016. The 33 revised full papers presented were carefully reviewed and selected from 100 submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, in medicine, and in process control, industry, and society.
Download or read book Automating the Design of Data Mining Algorithms written by Gisele L. Pappa and published by Springer Science & Business Media. This book was released on 2009-10-27 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.
Download or read book Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques written by Evangelos Triantaphyllou and published by Springer Science & Business Media. This book was released on 2006-09-10 with total page 784 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Feature Selection for Knowledge Discovery and Data Mining written by Huan Liu and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collected will not ever be examined, let alone be used to our advantages. Even with today's advanced computer technologies (e. g. , machine learning and data mining sys tems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Taking its simplest form, raw data are represented in feature-values. The size of a dataset can be measUJ·ed in two dimensions, number of features (N) and number of instances (P). Both Nand P can be enormously large. This enormity may cause serious problems to many data mining systems. Feature selection is one of the long existing methods that deal with these problems. Its objective is to select a minimal subset of features according to some reasonable criteria so that the original task can be achieved equally well, if not better. By choosing a minimal subset offeatures, irrelevant and redundant features are removed according to the criterion. When N is reduced, the data space shrinks and in a sense, the data set is now a better representative of the whole data population. If necessary, the reduction of N can also give rise to the reduction of P by eliminating duplicates.
Download or read book Data Mining written by Krzysztof J. Cios and published by Springer Science & Business Media. This book was released on 2007-10-05 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.
Download or read book Data Mining written by and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Mining With Decision Trees Theory And Applications 2nd Edition written by Oded Z Maimon and published by World Scientific. This book was released on 2014-09-03 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.This book invites readers to explore the many benefits in data mining that decision trees offer:
Download or read book Managing Data Mining Technologies in Organizations written by Parag C. Pendharkar and published by IGI Global. This book was released on 2003-01-01 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Portals present unique strategic challenges in the academic environment. Their conceptualization and design requires the input of campus constituents who seldom interact and whose interests are often opposite. The implementation of a portal requires a coordination of applications and databases controlled by different campus units at a level that may never before have been attempted at the institution. Building a portal is as much about constructing intra-campus bridges as it is about user interfaces and content. Designing Portals: Opportunities and Challenges discusses the current status of portals in higher education by providing insight into the role portals play in an institution's business and educational strategy, by taking the reader through the processes of conceptualization, design, and implementation of the portals (in different stages of development) at major universities and by offering insight from three producers of portal software systems in use at institutions of higher learning and elsewhere.
Download or read book Censorship Surveillance and Privacy Concepts Methodologies Tools and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2018-10-05 with total page 2215 pages. Available in PDF, EPUB and Kindle. Book excerpt: The censorship and surveillance of individuals, societies, and countries have been a long-debated ethical and moral issue. In consequence, it is vital to explore this controversial topic from all angles. Censorship, Surveillance, and Privacy: Concepts, Methodologies, Tools, and Applications is a vital reference source on the social, moral, religious, and political aspects of censorship and surveillance. It also explores the techniques of technologically supported censorship and surveillance. Highlighting a range of topics such as political censorship, propaganda, and information privacy, this multi-volume book is geared towards government officials, leaders, professionals, policymakers, media specialists, academicians, and researchers interested in the various facets of censorship and surveillance.
