Information Fusion in Data Mining

This book presents some recent fusion techniques that are currently in use in data mining, as well as data mining applications that use information fusion.

Information Fusion in Data Mining

Information fusion is becoming a major requirement in data mining and knowledge discovery in databases. This book presents some recent fusion techniques that are currently in use in data mining, as well as data mining applications that use information fusion. Special focus of the book is on information fusion in preprocessing, model building and information extraction with various applications.

Data Fusion and Data Mining for Power System Monitoring

Chu, W. W., Data Mining and Knowledge Discovery for Big Data—Methodologies
, Challenge, and Opportunities, ... Katz, O., Talmon, R., Lo, Y. L., Wu, H. T.,
Alternating diffusion maps for multimodal data fusion, Information Fusion, 45, 346
–360 ...

Data Fusion and Data Mining for Power System Monitoring

Data Fusion and Data Mining for Power System Monitoring provides a comprehensive treatment of advanced data fusion and data mining techniques for power system monitoring with focus on use of synchronized phasor networks. Relevant statistical data mining techniques are given, and efficient methods to cluster and visualize data collected from multiple sensors are discussed. Both linear and nonlinear data-driven mining and fusion techniques are reviewed, with emphasis on the analysis and visualization of massive distributed data sets. Challenges involved in realistic monitoring, visualization, and analysis of observation data from actual events are also emphasized, supported by examples of relevant applications. Features Focuses on systematic illustration of data mining and fusion in power systems Covers issues of standards used in the power industry for data mining and data analytics Applications to a wide range of power networks are provided including distribution and transmission networks Provides holistic approach to the problem of data mining and data fusion using cutting-edge methodologies and technologies Includes applications to massive spatiotemporal data from simulations and actual events

Fundamentals of Digital Manufacturing Science

Table 5.2 Comparison of data mining and information fusion Data processing
Data mining Information fusion method Knowledge generation process Discover
unknown from data Judge the mode instance by using the existing mode as ...

Fundamentals of Digital Manufacturing Science

The manufacturing industry will reap significant benefits from encouraging the development of digital manufacturing science and technology. Digital Manufacturing Science uses theorems, illustrations and tables to introduce the definition, theory architecture, main content, and key technologies of digital manufacturing science. Readers will be able to develop an in-depth understanding of the emergence and the development, the theoretical background, and the techniques and methods of digital manufacturing science. Furthermore, they will also be able to use the basic theories and key technologies described in Digital Manufacturing Science to solve practical engineering problems in modern manufacturing processes. Digital Manufacturing Science is aimed at advanced undergraduate and postgraduate students, academic researchers and researchers in the manufacturing industry. It allows readers to integrate the theories and technologies described with their own research works, and to propose new ideas and new methods to improve the theory and application of digital manufacturing science.

Data Mining and Knowledge Discovery Handbook

Chapter 47 INFORMATION FUSION Methods and Aggregation Operators Vicenc
Torra Institut d'Imestigacid en Intel. ligencia Artificial Abstract Information fusion
techniques are commonly applied in Data Mining and Knowledge Discovery.

Data Mining and Knowledge Discovery Handbook

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.

Nonlinear Integrals and Their Applications in Data Mining

The book is suitable as a text for graduate courses in mathematics, computer science, and information science. It is also useful to researchers in the relevant area.

Nonlinear Integrals and Their Applications in Data Mining

Regarding the set of all feature attributes in a given database as the universal set, this monograph discusses various nonadditive set functions that describe the interaction among the contributions from feature attributes towards a considered target attribute. Then, the relevant nonlinear integrals are investigated. These integrals can be applied as aggregation tools in information fusion and data mining, such as synthetic evaluation, nonlinear multiregressions, and nonlinear classifications. Some methods of fuzzification are also introduced for nonlinear integrals such that fuzzy data can be treated and fuzzy information is retrievable. The book is suitable as a text for graduate courses in mathematics, computer science, and information science. It is also useful to researchers in the relevant area.

Concepts Models and Tools for Information Fusion

[ 26 ] Nowak , C. , “ On Ontologies for Higher - Level Information Fusion , "
Proceedings of the ISIF 6th International Conference ... [ 33 ] Witten , I. , E. Frank ,
and J. Gray , Data Mining : Practical Machine Learning Tools and Techniques
with Java ...

Concepts  Models  and Tools for Information Fusion

In the process of information fusion technology, masses of live information are instantaneously integrated to create a coherent and precise picture of a rapidly evolving situation. This book brings together an international panel of leading experts that gives a fresh and cohesive perspective on this technologys models, methods, mathematics, and computer systems.

Advanced Techniques and Methods for Astronomical Image Handling

5 Third CCMA Workshop : ” From Information Fusion to Data Mining ” The third
CCMA workshop was held at the University of Granada , Spain , on April 18-19 ,
1997. The workshop , entitled " From Information Fusion to Data Mining " , dealt ...

Advanced Techniques and Methods for Astronomical Image Handling


Intelligent Data Mining and Fusion Systems in Agriculture

This book offers advanced students and entry-level professionals in agricultural science and engineering, geography and geoinformation science an in-depth overview of the connection between decision-making in agricultural operations and the ...

Intelligent Data Mining and Fusion Systems in Agriculture

Intelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion that have applications in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. Sections cover the combination of sensors with artificial intelligence architectures in precision agriculture, including algorithms, bio-inspired hierarchical neural maps, and novelty detection algorithms capable of detecting sudden changes in different conditions. This book offers advanced students and entry-level professionals in agricultural science and engineering, geography and geoinformation science an in-depth overview of the connection between decision-making in agricultural operations and the decision support features offered by advanced computational intelligence algorithms. Covers crop protection, automation in agriculture, artificial intelligence in agriculture, sensing and Internet of Things (IoTs) in agriculture Addresses AI use in weed management, disease detection, yield prediction and crop production Utilizes case studies to provide real-world insights and direction

Data Mining Rough Sets and Granular Computing

In this perspective, granular computing has a position of centrality in data mining. Another methodology which has high relevance to data mining and plays a central role in this volume is that of rough set theory.

Data Mining  Rough Sets and Granular Computing

During the past few years, data mining has grown rapidly in visibility and importance within information processing and decision analysis. This is par ticularly true in the realm of e-commerce, where data mining is moving from a "nice-to-have" to a "must-have" status. In a different though related context, a new computing methodology called granular computing is emerging as a powerful tool for the conception, analysis and design of information/intelligent systems. In essence, data mining deals with summarization of information which is resident in large data sets, while granular computing plays a key role in the summarization process by draw ing together points (objects) which are related through similarity, proximity or functionality. In this perspective, granular computing has a position of centrality in data mining. Another methodology which has high relevance to data mining and plays a central role in this volume is that of rough set theory. Basically, rough set theory may be viewed as a branch of granular computing. However, its applications to data mining have predated that of granular computing.

Modeling Decisions

This book covers the underlying science and application issues related to aggregation operators, focusing on tools used in practical applications that involve numerical information.

Modeling Decisions

This book covers the underlying science and application issues related to aggregation operators, focusing on tools used in practical applications that involve numerical information. It will thus be required reading for engineers, statisticians and computer scientists of all kinds. Starting with detailed introductions to information fusion and integration, measurement and probability theory, fuzzy sets, and functional equations, the authors then cover numerous topics in detail, including the synthesis of judgements, fuzzy measures, weighted means and fuzzy integrals.

Terrorism Informatics

This book is nothing less than a complete and comprehensive survey of the state-of-the-art of terrorism informatics. It covers the application of advanced methodologies and information fusion and analysis.

Terrorism Informatics

This book is nothing less than a complete and comprehensive survey of the state-of-the-art of terrorism informatics. It covers the application of advanced methodologies and information fusion and analysis. It also lays out techniques to acquire, integrate, process, analyze, and manage the diversity of terrorism-related information for international and homeland security-related applications. The book details three major areas of terrorism research: prevention, detection, and established governmental responses to terrorism. It systematically examines the current and ongoing research, including recent case studies and application of terrorism informatics techniques. The coverage then presents the critical and relevant social/technical areas to terrorism research including social, privacy, data confidentiality, and legal challenges.

Data Mining Multi Attribute Decision System Facilitating Decision Support Through Data Mining Technique by Hierarchical Multi Attribute Decision Models

Doctoral Thesis / Dissertation from the year 2020 in the subject Computer Science - Commercial Information Technology, Symbiosis International University, language: English, abstract: Data mining is coined one of the steps while discovering ...

Data Mining Multi Attribute Decision System  Facilitating Decision Support Through Data Mining Technique by Hierarchical Multi Attribute Decision Models

Doctoral Thesis / Dissertation from the year 2020 in the subject Computer Science - Commercial Information Technology, Symbiosis International University, language: English, abstract: Data mining is coined one of the steps while discovering insights from large amounts of data which may be stored in databases, data warehouses, or in other information repositories. Data mining is now playing a significant role in seeking a decision support to draw higher profits by the modern business world. Various researchers studied the benefits of data mining processes and its adoption by business organizations, but very few of them have discussed the success factors of decision support projects. The Research Hypothesis states the involvement of the decision tree while adopting accuracy of classification and while emphasizing the impact factor or importance of the attributes rather than the information gain. The concept of involvement of impact factor rather than just accuracy can be utilized in developing the new algorithm whose performance improves over the existing algorithms. We proposed a new algorithm which improves accuracy and contributing effectively in decision tree learning. We presented an algorithm that resolves the above stated problem of confliction of class. We have introduced the impact factor and classified impact factor to resolve the conflict situation. We have used data mining technique in facilitating the decision support with improved performance over its existing companion. We have also addressed the unique problem which have not been addressed before. Definitely, the fusion of data mining and decision support can contribute to problem-solving by enabling the vast hidden knowledge from data and knowledge received from experts. We have discussed a lot of work done in the field of decision support and hierarchical multi-attribute decision models. Ample amount of algorithms are available which are used to classify the data in datasets. Most algorithms use the concept of information gain for classification purpose. Some Lacking areas also exist. There is a need for an ideal algorithm for large datasets. There is a need for handling the missing values. There is a need for removing attribute bias towards choosing a random class when a conflict occurs. There is a need for decision support model which takes the advantages of hierarchical multi-attribute classification algorithms.

Data Mining VII

Very Large Data Bases ( VLDB ) , pages 487 - 499 . Johansson , U . , Niklasson ,
L . , König , R . , 2004 . Accuracy vs . Comprehensibility in Data Mining Models .
In Proceedings of the Seventh International Conference on Information Fusion ...

Data Mining VII

This book publishes papers from the Seventh International Conference on Data Mining and Information Systems. The book brings together state-of-the-art research results and practical development experiences from researchers and application developers from many different areas. The book covers topics as diverse as: Data Mining Themes such as Text Mining; Web Content, Structure and Usage Mining; Clustering Technologies; Categorisation Methods; Link Analysis; Data Preparation; Applications in Business, Industry and Government; Customer Relationship Management; Competitive Intelligence; Applications in Science and Engineering; Mining Geospatial Data; Business Process Management (BPM); Data Mining Inspired by Nature; National Security, and also Management Information Engineering such as Enterprise Information Systems; Applications of GIS and GPS; Applications of MIS; Remote Sensing; Information Systems Strategies and Methodologies; Hydro and Geo Informatics, Transportation; Bio Informatics; Biodiversity Information Systems.

FUSION

International Conference on Information Fusion. attack first . ... In a previous
paper a genetic algorithm ( GA ) was Ranging is a root concept that has a strong
used as a data mining function to determine parameters for relationship to " close
.

FUSION


Sensor Data Fusion and Integration of the Human Element

Waltz , E . L . , “ Information Understanding : Integrating Data Fusion and Data
Mining Processes ” , Proceedings of the 1998 IEEE International Symposium on
Circuits and Systems , Monterey , California , 31 May - 3 June 1998 . Antony , R .
T .

Sensor Data Fusion and Integration of the Human Element


Data Mining VI

Data Mining, Text Mining and Their Business Applications A. Zanasi, C. A.
Brebbia, Nelson F. F. Ebecken ... Through the use of cluster analysis and
information fusion , a streamlined grouping of human subjects , based on clothing
size , was ...

Data Mining VI

This book contains most of the papers presented at the Sixth International Conference on Data Mining held in Skiathos, Greece. Twenty-five countries from all the continents are represented in the papers published in the book, offering a real multinational and multicultural range of experiences and ideas.

Quality Aspects in Spatial Data Mining

This science deals with models of reality in a GIS, however, and not with reality itself. Therefore, spatial information processes are often impre

Quality Aspects in Spatial Data Mining

Describes the State-of-the-Art in Spatial Data Mining, Focuses on Data Quality Substantial progress has been made toward developing effective techniques for spatial information processing in recent years. This science deals with models of reality in a GIS, however, and not with reality itself. Therefore, spatial information processes are often imprecise, allowing for much interpretation of abstract figures and data. Quality Aspects in Spatial Data Mining introduces practical and theoretical solutions for making sense of the often chaotic and overwhelming amount of concrete data available to researchers. In this cohesive collection of peer-reviewed chapters, field authorities present the latest field advancements and cover such essential areas as data acquisition, geoinformation theory, spatial statistics, and dissemination. Each chapter debuts with an editorial preview of each topic from a conceptual, applied, and methodological point of view, making it easier for researchers to judge which information is most beneficial to their work. Chapters Evolve From Error Propagation and Spatial Statistics to Address Relevant Applications The book advises the use of granular computing as a means of circumventing spatial complexities. This counter-application to traditional computing allows for the calculation of imprecise probabilities – the kind of information that the spatial information systems community wrestles with much of the time. Under the editorial guidance of internationally respected geoinformatics experts, this indispensable volume addresses quality aspects in the entire spatial data mining process, from data acquisition to end user. It also alleviates what is often field researchers’ most daunting task by organizing the wealth of concrete spatial data available into one convenient source, thereby advancing the frontiers of spatial information systems.