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Temporal Data Mining via Unsupervised Ensemble Learning

Temporal Data Mining via Unsupervised Ensemble Learning
  • Author : Yun Yang
  • Publisher :Unknown
  • Release Date :2016-11-15
  • Total pages :172
  • ISBN : 9780128118412
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Summary : Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice. Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem. Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics. Includes fundamental concepts and knowledge, covering all key tasks and techniques of temporal data mining, i.e., temporal data representations, similarity measure, and mining tasks Concentrates on temporal data clustering tasks from different perspectives, including major algorithms from clustering algorithms and ensemble learning approaches Presents a rich blend of theory and practice, addressing seminal research ideas and looking at the technology from a practical point-of-view

Computational Intelligence and Its Applications

Computational Intelligence and Its Applications
  • Author : Abdelmalek Amine,Malek Mouhoub,Otmane Ait Mohamed,Bachir Djebbar
  • Publisher :Unknown
  • Release Date :2018-05-20
  • Total pages :670
  • ISBN : 9783319897431
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Summary : This book constitutes the refereed proceedings of the 6th IFIP TC 5 International Conference on Computational Intelligence and Its Applications, CIIA 2018, held in Oran, Algeria, in May 2018. The 56 full papers presented were carefully reviewed and selected from 202 submissions. They are organized in the following topical sections: data mining and information retrieval; evolutionary computation; machine learning; optimization; planning and scheduling; wireless communication and mobile computing; Internet of Things (IoT) and decision support systems; pattern recognition and image processing; and semantic web services.

Applications of Supervised and Unsupervised Ensemble Methods

Applications of Supervised and Unsupervised Ensemble Methods
  • Author : Oleg Okun
  • Publisher :Unknown
  • Release Date :2009-10-15
  • Total pages :268
  • ISBN : 9783642039997
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Summary : Expanding upon presentations at last year’s SUEMA (Supervised and Unsupervised Ensemble Methods and Applications) meeting, this volume explores recent developments in the field. Useful examples act as a guide for practitioners in computational intelligence.

Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining
  • Author : Zhi-Hua Zhou,Hang Li,Qiang Yang
  • Publisher :Unknown
  • Release Date :2007-06-21
  • Total pages :1161
  • ISBN : 9783540717010
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Summary : This book constitutes the refereed proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, held in Nanjing, China, May 2007. It covers new ideas, original research results and practical development experiences from all KDD-related areas including data mining, machine learning, data warehousing, data visualization, automatic scientific discovery, knowledge acquisition and knowledge-based systems.

Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining
  • Author : Hady W. Lauw
  • Publisher :Unknown
  • Release Date :2021
  • Total pages :229
  • ISBN : 9783030474263
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Summary :

Temporal Data Mining

Temporal Data Mining
  • Author : Theophano Mitsa
  • Publisher :Unknown
  • Release Date :2010-03-10
  • Total pages :395
  • ISBN : 1420089773
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Summary : Temporal data mining deals with the harvesting of useful information from temporal data. New initiatives in health care and business organizations have increased the importance of temporal information in data today. From basic data mining concepts to state-of-the-art advances, Temporal Data Mining covers the theory of this subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics, business and industrial applications, web usage mining, and spatiotemporal data mining. Along with various state-of-the-art algorithms, each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references. In the appendices, the author explains how data mining fits the overall goal of an organization and how these data can be interpreted for the purpose of characterizing a population. She also provides programs written in the Java language that implement some of the algorithms presented in the first chapter. Check out the author's blog at http://theophanomitsa.wordpress.com/

Intelligent Data Engineering and Automated Learning – IDEAL 2019

Intelligent Data Engineering and Automated Learning – IDEAL 2019
  • Author : Hujun Yin,David Camacho,Peter Tino,Antonio J. Tallón-Ballesteros,Ronaldo Menezes,Richard Allmendinger
  • Publisher :Unknown
  • Release Date :2019-11-07
  • Total pages :364
  • ISBN : 9783030336172
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Summary : This two-volume set of LNCS 11871 and 11872 constitutes the thoroughly refereed conference proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, held in Manchester, UK, in November 2019. The 94 full papers presented were carefully reviewed and selected from 149 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2019 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models (including neural networks, evolutionary computation and swarm intelligence), agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI.

Handbook of Statistical Analysis and Data Mining Applications

Handbook of Statistical Analysis and Data Mining Applications
  • Author : Robert Nisbet,Gary Miner,Ken Yale
  • Publisher :Unknown
  • Release Date :2017-11-09
  • Total pages :822
  • ISBN : 9780124166455
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Summary : Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :2003
  • Total pages :229
  • ISBN : UOM:39015047920973
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Summary :

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
  • Author : Frank Hutter
  • Publisher :Unknown
  • Release Date :2021
  • Total pages :229
  • ISBN : 9783030676612
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Summary :

Ensemble Methods in Data Mining

Ensemble Methods in Data Mining
  • Author : Giovanni Seni,John Elder
  • Publisher :Unknown
  • Release Date :2010-07-07
  • Total pages :126
  • ISBN : 9781608452859
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Summary : Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization -- today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods -- bagging, random forests, and boosting -- to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity. This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners. Table of Contents: Ensembles Discovered / Predictive Learning and Decision Trees / Model Complexity, Model Selection and Regularization / Importance Sampling and the Classic Ensemble Methods / Rule Ensembles and Interpretation Statistics / Ensemble Complexity

Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining
  • Author : Dinh Phung,Vincent S. Tseng,Geoffrey I. Webb,Bao Ho,Mohadeseh Ganji,Lida Rashidi
  • Publisher :Unknown
  • Release Date :2018-06-16
  • Total pages :835
  • ISBN : 9783319930404
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Summary : 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.

Nature-Inspired Computation and Swarm Intelligence

Nature-Inspired Computation and Swarm Intelligence
  • Author : Xin-She Yang
  • Publisher :Unknown
  • Release Date :2020-04-24
  • Total pages :442
  • ISBN : 9780128197141
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Summary : Nature-inspired computation and swarm intelligence have become popular and effective tools for solving problems in optimization, computational intelligence, soft computing and data science. Recently, the literature in the field has expanded rapidly, with new algorithms and applications emerging. Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is a timely reference giving a comprehensive review of relevant state-of-the-art developments in algorithms, theory and applications of nature-inspired algorithms and swarm intelligence. It reviews and documents the new developments, focusing on nature-inspired algorithms and their theoretical analysis, as well as providing a guide to their implementation. The book includes case studies of diverse real-world applications, balancing explanation of the theory with practical implementation. Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is suitable for researchers and graduate students in computer science, engineering, data science, and management science, who want a comprehensive review of algorithms, theory and implementation within the fields of nature inspired computation and swarm intelligence. Introduces nature-inspired algorithms and their fundamentals, including: particle swarm optimization, bat algorithm, cuckoo search, firefly algorithm, flower pollination algorithm, differential evolution and genetic algorithms as well as multi-objective optimization algorithms and others Provides a theoretical foundation and analyses of algorithms, including: statistical theory and Markov chain theory on the convergence and stability of algorithms, dynamical system theory, benchmarking of optimization, no-free-lunch theorems, and a generalized mathematical framework Includes a diversity of case studies of real-world applications: feature selection, clustering and classification, tuning of restricted Boltzmann machines, travelling salesman problem, classification of white blood cells, music generation by artificial intelligence, swarm robots, neural networks, engineering designs and others

Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining
  • Author : Tu Bao Ho,David Cheung
  • Publisher :Unknown
  • Release Date :2005-05-10
  • Total pages :864
  • ISBN : 9783540260769
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Summary : This book constitutes the refereed proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2005, held in Hanoi, Vietnam, in May 2005. The 48 revised full papers and 49 revised short papers presented together with abstracts or extended abstracts of 3 invited talks were carefully reviewed and selected from 327 submissions. The papers are organized in topical sections on theoretical foundations, association rules, biomedical domains, classification and ranking, clustering, dynamic data mining, graphical model discovery, high dimensional data, integration of data warehousing, knowledge management, machine learning, novel algorithms, spatial data, temporal data, and text and Web data mining.

Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
  • Author : Jiawei Han,Jian Pei,Micheline Kamber
  • Publisher :Unknown
  • Release Date :2011-06-09
  • Total pages :744
  • ISBN : 0123814804
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Summary : Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Advanced Data Mining and Applications

Advanced Data Mining and Applications
  • Author : Jianxin Li,Sen Wang,Shaowen Qin,Xue Li,Shuliang Wang
  • Publisher :Unknown
  • Release Date :2020-01-15
  • Total pages :893
  • ISBN : 9783030352318
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Summary : This book constitutes the proceedings of the 15th International Conference on Advanced Data Mining and Applications, ADMA 2019, held in Dalian, China in November 2019. The 39 full papers presented together with 26 short papers and 2 demo papers were carefully reviewed and selected from 170 submissions. The papers were organized in topical sections named: Data Mining Foundations; Classification and Clustering Methods; Recommender Systems; Social Network and Social Media; Behavior Modeling and User Profiling; Text and Multimedia Mining; Spatial-Temporal Data; Medical and Healthcare Data/Decision Analytics; and Other Applications.

Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining
  • Author : Jinho Kim,Kyuseok Shim,Longbing Cao,Jae-Gil Lee,Xuemin Lin,Yang-Sae Moon
  • Publisher :Unknown
  • Release Date :2017-04-25
  • Total pages :841
  • ISBN : 9783319574547
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Summary : This two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction.

Managing Time in Relational Databases

Managing Time in Relational Databases
  • Author : Tom Johnston,Randall Weis
  • Publisher :Unknown
  • Release Date :2010-08-19
  • Total pages :512
  • ISBN : 0080963374
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Summary : Managing Time in Relational Databases: How to Design, Update and Query Temporal Data introduces basic concepts that will enable businesses to develop their own framework for managing temporal data. It discusses the management of uni-temporal and bi-temporal data in relational databases, so that they can be seamlessly accessed together with current data; the encapsulation of temporal data structures and processes; ways to implement temporal data management as an enterprise solution; and the internalization of pipeline datasets. The book is organized into three parts. Part 1 traces the history of temporal data management and presents a taxonomy of bi-temporal data management methods. Part 2 provides an introduction to Asserted Versioning, covering the origins of Asserted Versioning; core concepts of Asserted Versioning; the schema common to all asserted version tables, as well as the various diagrams and notations used in the rest of the book; and how the basic scenario works when the target of that activity is an asserted version table. Part 3 deals with designing, maintaining, and querying asserted version databases. It discusses the design of Asserted Versioning databases; temporal transactions; deferred assertions and other pipeline datasets; Allen relationships; and optimizing Asserted Versioning databases. Integrates an enterprise-wide viewpoint with a strong conceptual model of temporal data management allowing for realistic implementation of database application development. Provides a true practical guide to the different possible methods of time-oriented databases with techniques of using existing funtionality to solve real world problems within an enterprise data architecture environment. Written by IT professionals for IT professionals, this book employs a heavily example-driven approach which reinforces learning by showing the results of puting the techniques discussed into practice.

Quantum Machine Learning

Quantum Machine Learning
  • Author : Peter Wittek
  • Publisher :Unknown
  • Release Date :2014-09-10
  • Total pages :176
  • ISBN : 9780128010990
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Summary : Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications. Bridges the gap between abstract developments in quantum computing with the applied research on machine learning Provides the theoretical minimum of machine learning, quantum mechanics, and quantum computing Gives step-by-step guidance to a broader understanding of this emergent interdisciplinary body of research

Data Analysis, Machine Learning and Applications

Data Analysis, Machine Learning and Applications
  • Author : Christine Preisach,Hans Burkhardt,Lars Schmidt-Thieme,Reinhold Decker
  • Publisher :Unknown
  • Release Date :2008-04-13
  • Total pages :719
  • ISBN : 354078246X
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Summary : Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007.

Applications of Data Mining in Computer Security

Applications of Data Mining in Computer Security
  • Author : Daniel Barbará,Sushil Jajodia
  • Publisher :Unknown
  • Release Date :2002-05-31
  • Total pages :252
  • ISBN : 1402070543
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Summary : Data mining is becoming a pervasive technology in activities as diverse as using historical data to predict the success of a marketing campaign, looking for patterns in financial transactions to discover illegal activities or analyzing genome sequences. From this perspective, it was just a matter of time for the discipline to reach the important area of computer security. Applications Of Data Mining In Computer Security presents a collection of research efforts on the use of data mining in computer security. Applications Of Data Mining In Computer Security concentrates heavily on the use of data mining in the area of intrusion detection. The reason for this is twofold. First, the volume of data dealing with both network and host activity is so large that it makes it an ideal candidate for using data mining techniques. Second, intrusion detection is an extremely critical activity. This book also addresses the application of data mining to computer forensics. This is a crucial area that seeks to address the needs of law enforcement in analyzing the digital evidence.