Data Analysis And Pattern Recognition In Multiple Databases

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Data Analysis and Pattern Recognition in Multiple Databases

Animesh Adhikari,Jhimli Adhikari,Witold Pedrycz
Data Analysis and Pattern Recognition in Multiple Databases Book PDF

Author : Animesh Adhikari,Jhimli Adhikari,Witold Pedrycz
Publisher : Springer Science & Business Media
Page : 238 pages
File Size : 41,5 Mb
Release : 2013-12-09
Category : Technology & Engineering
ISBN : 9783319034102

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Data Analysis and Pattern Recognition in Multiple Databases by Animesh Adhikari,Jhimli Adhikari,Witold Pedrycz Pdf

Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.

Advances in Knowledge Discovery in Databases

Animesh Adhikari,Jhimli Adhikari
Advances in Knowledge Discovery in Databases Book PDF

Author : Animesh Adhikari,Jhimli Adhikari
Publisher : Springer
Page : 370 pages
File Size : 52,8 Mb
Release : 2014-12-27
Category : Technology & Engineering
ISBN : 9783319132129

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Advances in Knowledge Discovery in Databases by Animesh Adhikari,Jhimli Adhikari Pdf

This book presents recent advances in Knowledge discovery in databases (KDD) with a focus on the areas of market basket database, time-stamped databases and multiple related databases. Various interesting and intelligent algorithms are reported on data mining tasks. A large number of association measures are presented, which play significant roles in decision support applications. This book presents, discusses and contrasts new developments in mining time-stamped data, time-based data analyses, the identification of temporal patterns, the mining of multiple related databases, as well as local patterns analysis.

Pattern Recognition Algorithms for Data Mining

Sankar K. Pal,Pabitra Mitra
Pattern Recognition Algorithms for Data Mining Book PDF

Author : Sankar K. Pal,Pabitra Mitra
Publisher : CRC Press
Page : 280 pages
File Size : 50,9 Mb
Release : 2004-05-27
Category : Computers
ISBN : 9780203998076

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Pattern Recognition Algorithms for Data Mining by Sankar K. Pal,Pabitra Mitra Pdf

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, me

Machine Learning and Data Mining in Pattern Recognition

Petra Perner,Maria Petrou
Machine Learning and Data Mining in Pattern Recognition Book PDF

Author : Petra Perner,Maria Petrou
Publisher : Springer
Page : 224 pages
File Size : 44,8 Mb
Release : 2003-06-26
Category : Computers
ISBN : 9783540480976

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Machine Learning and Data Mining in Pattern Recognition by Petra Perner,Maria Petrou Pdf

The field of machine learning and data mining in connection with pattern recognition enjoys growing popularity and attracts many researchers. Automatic pattern recognition systems have proven successful in many applications. The wide use of these systems depends on their ability to adapt to changing environmental conditions and to deal with new objects. This requires learning capabilities on the parts of these systems. The exceptional attraction of learning in pattern recognition lies in the specific data themselves and the different stages at which they get processed in a pattern recognition system. This results a specific branch within the field of machine learning. At the workshop, were presented machine learning approaches for image pre-processing, image segmentation, recognition and interpretation. Machine learning systems were shown on applications such as document analysis and medical image analysis. Many databases are developed that contain multimedia sources such as images, measurement protocols, and text documents. Such systems should be able to retrieve these sources by content. That requires specific retrieval and indexing strategies for images and signals. Higher quality database contents can be achieved if it were possible to mine these databases for their underlying information. Such mining techniques have to consider the specific characteristic of the image sources. The field of mining multimedia databases is just starting out. We hope that our workshop can attract many other researchers to this subject.

Pattern Recognition and Data Mining

Sameer Singh,Maneesha Singh,Chid Apte,Petra Perner
Pattern Recognition and Data Mining Book PDF

Author : Sameer Singh,Maneesha Singh,Chid Apte,Petra Perner
Publisher : Springer Science & Business Media
Page : 713 pages
File Size : 43,8 Mb
Release : 2005-08-18
Category : Computers
ISBN : 9783540287575

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Pattern Recognition and Data Mining by Sameer Singh,Maneesha Singh,Chid Apte,Petra Perner Pdf

The two volume set LNCS 3686 and LNCS 3687 constitutes the refereed proceedings of the Third International Conference on Advances in Pattern Recognition, ICAPR 2005, held in Bath, UK in August 2005. The papers submitted to ICAPR 2005 were thoroughly reviewed by up to three referees per paper and less than 40% of the submitted papers were accepted. The first volume includes 73 contributions related to Pattern Recognition and Data Mining (which included papers from the tracks of pattern recognition methods, knowledge and learning, and data mining); topics addressed are pattern recognition, data mining, signal processing and OCR/ document analysis. The second volume contains 87 contributions related to Pattern Recognition and Image Analysis (which included papers from the applications track) and deals with security and surveillance, biometrics, image processing and medical imaging. It also contains papers from the Workshop on Pattern Recognition for Crime Prevention.

Machine Interpretation of Patterns

Rajat K. De,Ashish Ghosh,Deba Prasad Mandal
Machine Interpretation of Patterns Book PDF

Author : Rajat K. De,Ashish Ghosh,Deba Prasad Mandal
Publisher : World Scientific
Page : 316 pages
File Size : 42,8 Mb
Release : 2010
Category : Computers
ISBN : 9789814299190

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Machine Interpretation of Patterns by Rajat K. De,Ashish Ghosh,Deba Prasad Mandal Pdf

1. Combining information with a Bayesian multi-class multi-kernel pattern recognition machine / T. Damoulas and M.A. Girolami -- 2. Image quality assessment based on weighted perceptual features / D.V. Rao and L.P. Reddy -- 3. Quasi-reversible two-dimension fractional differentiation for image entropy reduction / A. Nakib [und weitere] -- 4. Parallel genetic algorithm based clustering for object and background classification / P. Kanungo, P.K. Nanda and A. Ghosh -- 5. Bipolar fuzzy spatial information : first operations in the mathematical morphology setting / I. Bloch -- 6. Approaches to intelligent information retrieval / G. Pasi -- 7. Retrieval of on-line signatures / H.N. Prakash and D.S. Guru -- 8. A two stage recognition scheme for offline handwritten Devanagari Words / B. Shaw and S.K. Parui -- 9. Fall detection from a video in the presence of multiple persons / V. Vishwakarma, S. Sural and C. Mandal -- 10. Fusion of GIS and SAR statistical features for earthquake damage mapping at the block scale / G. Trianni [und weitere] -- 11. Intelligent surveillance and Pose-invariant 2D face classification / B.C. Lovell, C. Sanderson and T. Shan -- 12. Simple machine learning approaches to safety-related systems / C. Moewes, C. Otte and R. Kruse -- 13. Nonuniform multi level crossings for signal reconstruction / N. Poojary, H. Kumar and A. Rao -- 14. Adaptive web services brokering / K.M. Gupta and D.W. Aha -- 15. Granular support vector machine based method for prediction of solubility of proteins on over expression in Escherichia Coli and breast cancer classification / P. Kumar, B.D. Kulkarni and V.K. Jayaraman

Guide to Intelligent Data Analysis

Michael R. Berthold,Christian Borgelt,Frank Höppner,Frank Klawonn
Guide to Intelligent Data Analysis Book PDF

Author : Michael R. Berthold,Christian Borgelt,Frank Höppner,Frank Klawonn
Publisher : Springer Science & Business Media
Page : 399 pages
File Size : 43,5 Mb
Release : 2010-06-23
Category : Computers
ISBN : 9781848822603

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Guide to Intelligent Data Analysis by Michael R. Berthold,Christian Borgelt,Frank Höppner,Frank Klawonn Pdf

Each passing year bears witness to the development of ever more powerful computers, increasingly fast and cheap storage media, and even higher bandwidth data connections. This makes it easy to believe that we can now – at least in principle – solve any problem we are faced with so long as we only have enough data. Yet this is not the case. Although large databases allow us to retrieve many different single pieces of information and to compute simple aggregations, general patterns and regularities often go undetected. Furthermore, it is exactly these patterns, regularities and trends that are often most valuable. To avoid the danger of “drowning in information, but starving for knowledge” the branch of research known as data analysis has emerged, and a considerable number of methods and software tools have been developed. However, it is not these tools alone but the intelligent application of human intuition in combination with computational power, of sound background knowledge with computer-aided modeling, and of critical reflection with convenient automatic model construction, that results in successful intelligent data analysis projects. Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems. Topics and features: guides the reader through the process of data analysis, following the interdependent steps of project understanding, data understanding, data preparation, modeling, and deployment and monitoring; equips the reader with the necessary information in order to obtain hands-on experience of the topics under discussion; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; includes numerous examples using R and KNIME, together with appendices introducing the open source software; integrates illustrations and case-study-style examples to support pedagogical exposition. This practical and systematic textbook/reference for graduate and advanced undergraduate students is also essential reading for all professionals who face data analysis problems. Moreover, it is a book to be used following one’s exploration of it. Dr. Michael R. Berthold is Nycomed-Professor of Bioinformatics and Information Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is Principal Researcher at the Intelligent Data Analysis and Graphical Models Research Unit of the European Centre for Soft Computing, Spain. Dr. Frank Höppner is Professor of Information Systems at Ostfalia University of Applied Sciences, Germany. Dr. Frank Klawonn is a Professor in the Department of Computer Science and Head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany. He is also Head of the Bioinformatics and Statistics group at the Helmholtz Centre for Infection Research, Braunschweig, Germany.

Pattern Recognition and Data Analysis with Applications

Deepak Gupta,Rajat Subhra Goswami,Subhasish Banerjee,M. Tanveer,Ram Bilas Pachori
Pattern Recognition and Data Analysis with Applications Book PDF

Author : Deepak Gupta,Rajat Subhra Goswami,Subhasish Banerjee,M. Tanveer,Ram Bilas Pachori
Publisher : Springer Nature
Page : 816 pages
File Size : 41,6 Mb
Release : 2022-09-01
Category : Technology & Engineering
ISBN : 9789811915208

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Pattern Recognition and Data Analysis with Applications by Deepak Gupta,Rajat Subhra Goswami,Subhasish Banerjee,M. Tanveer,Ram Bilas Pachori Pdf

This book covers latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing and their applications in real world. The topics covered in machine learning involves feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN) and other areas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use of geometric techniques, scene understanding and modelling from video, 3D object recognition, localization and tracking, medical image analysis and so on. Computational learning theory involves different kinds of learning like incremental, online, reinforcement, manifold, multi-task, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. Additionally, it covers the recent developments to network intelligence for analyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In the last, it includes the progress in signal processing to process the normal and abnormal categories of real-world signals, for instance signals generated from IoT devices, smart systems, speech, videos, etc., and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG) and electromyogram (EMG).

Machine Learning and Data Mining in Pattern Recognition

Petra Perner,Maria Petrou
Machine Learning and Data Mining in Pattern Recognition Book PDF

Author : Petra Perner,Maria Petrou
Publisher : Springer
Page : 224 pages
File Size : 46,8 Mb
Release : 1999-09-08
Category : Computers
ISBN : 3540665994

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Machine Learning and Data Mining in Pattern Recognition by Petra Perner,Maria Petrou Pdf

The field of machine learning and data mining in connection with pattern recognition enjoys growing popularity and attracts many researchers. Automatic pattern recognition systems have proven successful in many applications. The wide use of these systems depends on their ability to adapt to changing environmental conditions and to deal with new objects. This requires learning capabilities on the parts of these systems. The exceptional attraction of learning in pattern recognition lies in the specific data themselves and the different stages at which they get processed in a pattern recognition system. This results a specific branch within the field of machine learning. At the workshop, were presented machine learning approaches for image pre-processing, image segmentation, recognition and interpretation. Machine learning systems were shown on applications such as document analysis and medical image analysis. Many databases are developed that contain multimedia sources such as images, measurement protocols, and text documents. Such systems should be able to retrieve these sources by content. That requires specific retrieval and indexing strategies for images and signals. Higher quality database contents can be achieved if it were possible to mine these databases for their underlying information. Such mining techniques have to consider the specific characteristic of the image sources. The field of mining multimedia databases is just starting out. We hope that our workshop can attract many other researchers to this subject.

Pattern Recognition And Big Data

Pal Sankar Kumar,Pal Amita
Pattern Recognition And Big Data Book PDF

Author : Pal Sankar Kumar,Pal Amita
Publisher : World Scientific
Page : 876 pages
File Size : 44,5 Mb
Release : 2016-12-15
Category : Computers
ISBN : 9789813144569

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Pattern Recognition And Big Data by Pal Sankar Kumar,Pal Amita Pdf

Containing twenty six contributions by experts from all over the world, this book presents both research and review material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, linguistic, fuzzy-set-theoretic, neural, evolutionary computing and rough-set-theoretic to hybrid soft computing, with significant real-life applications. Pattern Recognition and Big Data provides state-of-the-art classical and modern approaches to pattern recognition and mining, with extensive real life applications. The book describes efficient soft and robust machine learning algorithms and granular computing techniques for data mining and knowledge discovery; and the issues associated with handling Big Data. Application domains considered include bioinformatics, cognitive machines (or machine mind developments), biometrics, computer vision, the e-nose, remote sensing and social network analysis.

Machine Learning and Data Mining in Pattern Recognition

Petra Perner,Maria Petrou
Machine Learning and Data Mining in Pattern Recognition Book PDF

Author : Petra Perner,Maria Petrou
Publisher : Springer
Page : 0 pages
File Size : 40,7 Mb
Release : 2003-06-26
Category : Computers
ISBN : 3540480978

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Machine Learning and Data Mining in Pattern Recognition by Petra Perner,Maria Petrou Pdf

The field of machine learning and data mining in connection with pattern recognition enjoys growing popularity and attracts many researchers. Automatic pattern recognition systems have proven successful in many applications. The wide use of these systems depends on their ability to adapt to changing environmental conditions and to deal with new objects. This requires learning capabilities on the parts of these systems. The exceptional attraction of learning in pattern recognition lies in the specific data themselves and the different stages at which they get processed in a pattern recognition system. This results a specific branch within the field of machine learning. At the workshop, were presented machine learning approaches for image pre-processing, image segmentation, recognition and interpretation. Machine learning systems were shown on applications such as document analysis and medical image analysis. Many databases are developed that contain multimedia sources such as images, measurement protocols, and text documents. Such systems should be able to retrieve these sources by content. That requires specific retrieval and indexing strategies for images and signals. Higher quality database contents can be achieved if it were possible to mine these databases for their underlying information. Such mining techniques have to consider the specific characteristic of the image sources. The field of mining multimedia databases is just starting out. We hope that our workshop can attract many other researchers to this subject.

Advanced Methods for Knowledge Discovery from Complex Data

Ujjwal Maulik,Lawrence B. Holder,Diane J. Cook
Advanced Methods for Knowledge Discovery from Complex Data Book PDF

Author : Ujjwal Maulik,Lawrence B. Holder,Diane J. Cook
Publisher : Springer Science & Business Media
Page : 375 pages
File Size : 49,8 Mb
Release : 2006-05-06
Category : Computers
ISBN : 9781846282843

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Advanced Methods for Knowledge Discovery from Complex Data by Ujjwal Maulik,Lawrence B. Holder,Diane J. Cook Pdf

The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters.

Pattern Recognition and Machine Intelligence

Sanghamitra Bandyopadhyay
Pattern Recognition and Machine Intelligence Book PDF

Author : Sanghamitra Bandyopadhyay
Publisher : Springer Science & Business Media
Page : 831 pages
File Size : 55,8 Mb
Release : 2005-12-09
Category : Computers
ISBN : 9783540305064

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Pattern Recognition and Machine Intelligence by Sanghamitra Bandyopadhyay Pdf

This book constitutes the refereed proceedings of the First International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005, held in Kolkata, India in December 2005. The 108 revised papers presented together with 6 keynote talks and 14 invited papers were carefully reviewed and selected from 250 submissions. The papers are organized in topical sections on clustering, feature selection and learning, classification, neural networks and applications, fuzzy logic and applications, optimization and representation, image processing and analysis, video processing and computer vision, image retrieval and data mining, bioinformatics application, Web intelligence and genetic algorithms, as well as rough sets, case-based reasoning and knowledge discovery.

Advances in Machine Learning and Data Science

Damodar Reddy Edla,Pawan Lingras,Venkatanareshbabu K.
Advances in Machine Learning and Data Science Book PDF

Author : Damodar Reddy Edla,Pawan Lingras,Venkatanareshbabu K.
Publisher : Springer
Page : 380 pages
File Size : 46,5 Mb
Release : 2018-05-16
Category : Technology & Engineering
ISBN : 9789811085697

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Advances in Machine Learning and Data Science by Damodar Reddy Edla,Pawan Lingras,Venkatanareshbabu K. Pdf

The Volume of “Advances in Machine Learning and Data Science - Recent Achievements and Research Directives” constitutes the proceedings of First International Conference on Latest Advances in Machine Learning and Data Science (LAMDA 2017). The 37 regular papers presented in this volume were carefully reviewed and selected from 123 submissions. These days we find many computer programs that exhibit various useful learning methods and commercial applications. Goal of machine learning is to develop computer programs that can learn from experience. Machine learning involves knowledge from various disciplines like, statistics, information theory, artificial intelligence, computational complexity, cognitive science and biology. For problems like handwriting recognition, algorithms that are based on machine learning out perform all other approaches. Both machine learning and data science are interrelated. Data science is an umbrella term to be used for techniques that clean data and extract useful information from data. In field of data science, machine learning algorithms are used frequently to identify valuable knowledge from commercial databases containing records of different industries, financial transactions, medical records, etc. The main objective of this book is to provide an overview on latest advancements in the field of machine learning and data science, with solutions to problems in field of image, video, data and graph processing, pattern recognition, data structuring, data clustering, pattern mining, association rule based approaches, feature extraction techniques, neural networks, bio inspired learning and various machine learning algorithms.

Machine Learning and Data Mining in Pattern Recognition

Petra Perner
Machine Learning and Data Mining in Pattern Recognition Book PDF

Author : Petra Perner
Publisher : Springer
Page : 536 pages
File Size : 41,7 Mb
Release : 2014-07-17
Category : Computers
ISBN : 9783319089799

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Machine Learning and Data Mining in Pattern Recognition by Petra Perner Pdf

This book constitutes the refereed proceedings of the 10th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2014, held in St. Petersburg, Russia in July 2014. The 40 full papers presented were carefully reviewed and selected from 128 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.