Knowledge Discovery In Multiple Databases

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Knowledge Discovery in Multiple Databases

Shichao Zhang,Chengqi Zhang,Xindong Wu
Knowledge Discovery in Multiple Databases Book PDF

Author : Shichao Zhang,Chengqi Zhang,Xindong Wu
Publisher : Springer Science & Business Media
Page : 233 pages
File Size : 51,7 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9780857293886

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Knowledge Discovery in Multiple Databases by Shichao Zhang,Chengqi Zhang,Xindong Wu Pdf

Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining.

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 : 46,5 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.

Knowledge Discovery in Inductive Databases

Saso Dzeroski,Jan Struyf
Knowledge Discovery in Inductive Databases Book PDF

Author : Saso Dzeroski,Jan Struyf
Publisher : Springer
Page : 301 pages
File Size : 41,8 Mb
Release : 2007-09-29
Category : Computers
ISBN : 9783540755494

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Knowledge Discovery in Inductive Databases by Saso Dzeroski,Jan Struyf Pdf

This book constitutes the thoroughly refereed joint postproceedings of the 5th International Workshop on Knowledge Discovery in Inductive Databases, KDID 2006, held in association with ECML/PKDD. Bringing together the fields of databases, machine learning, and data mining, the papers address various current topics in knowledge discovery and data mining in the framework of inductive databases such as constraint-based mining, database technology and inductive querying.

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 : 45,8 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.

Developing Multi-Database Mining Applications

Animesh Adhikari,Pralhad Ramachandrarao,Witold Pedrycz
Developing Multi Database Mining Applications Book PDF

Author : Animesh Adhikari,Pralhad Ramachandrarao,Witold Pedrycz
Publisher : Springer Science & Business Media
Page : 134 pages
File Size : 50,8 Mb
Release : 2010-06-14
Category : Computers
ISBN : 9781849960441

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Developing Multi-Database Mining Applications by Animesh Adhikari,Pralhad Ramachandrarao,Witold Pedrycz Pdf

Multi-database mining has been recognized recently as an important and strategically essential area of research in data mining. In this book, we discuss various issues regarding the systematic and efficient development of multi-database mining applications. It explains how systematically one could prepare data warehouses at different branches. As appropriate multi-database mining technique is essential to develop better applications. Also, the efficiency of a multi-database mining application could be improved by processing more patterns in the application. A faster algorithm could also play an important role in developing a better application. Thus the efficiency of a multi-database mining application could be enhanced by choosing an appropriate multi-database mining model, an appropriate pattern synthesizing technique, a better pattern representation technique, and an efficient algorithm for solving the problem. This book illustrates each of these issues either in the context of a specific problem, or in general.

Knowledge Discovery in Inductive Databases

Arno Siebes
Knowledge Discovery in Inductive Databases Book PDF

Author : Arno Siebes
Publisher : Springer
Page : 200 pages
File Size : 55,7 Mb
Release : 2005-02-09
Category : Computers
ISBN : 9783540318415

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Knowledge Discovery in Inductive Databases by Arno Siebes Pdf

This book constitutes the thoroughly refereed joint postproceedings of the Third International Workshop on Knowledge Discovery in Inductive Databases, KDID 2004, held in Pisa, Italy in September 2004 in association with ECML/PKDD. Inductive Databases support data mining and the knowledge discovery process in a natural way. In addition to usual data, an inductive database also contains inductive generalizations, like patterns and models extracted from the data. This book presents nine revised full papers selected from 23 submissions during two rounds of reviewing and improvement together with one invited paper. Various current topics in knowledge discovery and data mining in the framework of inductive databases are addressed.

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 : 50,7 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.

Machine Learning and Knowledge Discovery in Databases

Michelangelo Ceci,Jaakko Hollmén,Ljupčo Todorovski,Celine Vens,Sašo Džeroski
Machine Learning and Knowledge Discovery in Databases Book PDF

Author : Michelangelo Ceci,Jaakko Hollmén,Ljupčo Todorovski,Celine Vens,Sašo Džeroski
Publisher : Springer
Page : 866 pages
File Size : 44,9 Mb
Release : 2017-12-29
Category : Computers
ISBN : 9783319712468

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Machine Learning and Knowledge Discovery in Databases by Michelangelo Ceci,Jaakko Hollmén,Ljupčo Todorovski,Celine Vens,Sašo Džeroski Pdf

The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.

Principles of Data Mining and Knowledge Discovery

Jan Zytkow,Jan Rauch
Principles of Data Mining and Knowledge Discovery Book PDF

Author : Jan Zytkow,Jan Rauch
Publisher : Springer
Page : 593 pages
File Size : 41,5 Mb
Release : 2004-06-08
Category : Computers
ISBN : 9783540482475

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Principles of Data Mining and Knowledge Discovery by Jan Zytkow,Jan Rauch Pdf

This book constitutes the refereed proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD'99, held in Prague, Czech Republic in September 1999. The 28 revised full papers and 48 poster presentations were carefully reviewed and selected from 106 full papers submitted. The papers are organized in topical sections on time series, applications, taxonomies and partitions, logic methods, distributed and multirelational databases, text mining and feature selection, rules and induction, and interesting and unusual issues.

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
Page : 0 pages
File Size : 43,6 Mb
Release : 2005-11-09
Category : Computers
ISBN : 1852339896

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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 followingchapters.

Machine Learning and Knowledge Discovery in Databases

Peggy Cellier,Kurt Driessens
Machine Learning and Knowledge Discovery in Databases Book PDF

Author : Peggy Cellier,Kurt Driessens
Publisher : Springer Nature
Page : 755 pages
File Size : 54,5 Mb
Release : 2020-03-27
Category : Computers
ISBN : 9783030438876

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Machine Learning and Knowledge Discovery in Databases by Peggy Cellier,Kurt Driessens Pdf

This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019; Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with Knowledge Graphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019.

Machine Learning and Knowledge Discovery in Databases

Michelangelo Ceci,Jaakko Hollmén,Ljupčo Todorovski,Celine Vens,Sašo Džeroski
Machine Learning and Knowledge Discovery in Databases Book PDF

Author : Michelangelo Ceci,Jaakko Hollmén,Ljupčo Todorovski,Celine Vens,Sašo Džeroski
Publisher : Springer
Page : 852 pages
File Size : 52,9 Mb
Release : 2017-12-29
Category : Computers
ISBN : 9783319712499

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Machine Learning and Knowledge Discovery in Databases by Michelangelo Ceci,Jaakko Hollmén,Ljupčo Todorovski,Celine Vens,Sašo Džeroski Pdf

The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.

Principles of Data Mining and Knowledge Discovery

Jan Zytkow,Jan Rauch
Principles of Data Mining and Knowledge Discovery Book PDF

Author : Jan Zytkow,Jan Rauch
Publisher : Springer Science & Business Media
Page : 608 pages
File Size : 55,8 Mb
Release : 1999-09-01
Category : Computers
ISBN : 9783540664901

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Principles of Data Mining and Knowledge Discovery by Jan Zytkow,Jan Rauch Pdf

This book constitutes the refereed proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD'99, held in Prague, Czech Republic in September 1999. The 28 revised full papers and 48 poster presentations were carefully reviewed and selected from 106 full papers submitted. The papers are organized in topical sections on time series, applications, taxonomies and partitions, logic methods, distributed and multirelational databases, text mining and feature selection, rules and induction, and interesting and unusual issues.

Advances in Knowledge Discovery and Data Mining

Usama M. Fayyad
Advances in Knowledge Discovery and Data Mining Book PDF

Author : Usama M. Fayyad
Publisher : Unknown
Page : 638 pages
File Size : 46,8 Mb
Release : 1996
Category : Computers
ISBN : UOM:39015037286955

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Advances in Knowledge Discovery and Data Mining by Usama M. Fayyad Pdf

Eight sections of this book span fundamental issues of knowledge discovery, classification and clustering, trend and deviation analysis, dependency derivation, integrated discovery systems, augumented database systems and application case studies. The appendices provide a list of terms used in the literature of the field of data mining and knowledge discovery in databases, and a list of online resources for the KDD researcher.

Knowledge Discovery and Measures of Interest

Robert J. Hilderman,Howard J. Hamilton
Knowledge Discovery and Measures of Interest Book PDF

Author : Robert J. Hilderman,Howard J. Hamilton
Publisher : Springer Science & Business Media
Page : 170 pages
File Size : 47,6 Mb
Release : 2013-03-14
Category : Computers
ISBN : 9781475732832

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Knowledge Discovery and Measures of Interest by Robert J. Hilderman,Howard J. Hamilton Pdf

Knowledge Discovery and Measures of Interest is a reference book for knowledge discovery researchers, practitioners, and students. The knowledge discovery researcher will find that the material provides a theoretical foundation for measures of interest in data mining applications where diversity measures are used to rank summaries generated from databases. The knowledge discovery practitioner will find solid empirical evidence on which to base decisions regarding the choice of measures in data mining applications. The knowledge discovery student in a senior undergraduate or graduate course in databases and data mining will find the book is a good introduction to the concepts and techniques of measures of interest. In Knowledge Discovery and Measures of Interest, we study two closely related steps in any knowledge discovery system: the generation of discovered knowledge; and the interpretation and evaluation of discovered knowledge. In the generation step, we study data summarization, where a single dataset can be generalized in many different ways and to many different levels of granularity according to domain generalization graphs. In the interpretation and evaluation step, we study diversity measures as heuristics for ranking the interestingness of the summaries generated. The objective of this work is to introduce and evaluate a technique for ranking the interestingness of discovered patterns in data. It consists of four primary goals: To introduce domain generalization graphs for describing and guiding the generation of summaries from databases. To introduce and evaluate serial and parallel algorithms that traverse the domain generalization space described by the domain generalization graphs. To introduce and evaluate diversity measures as heuristic measures of interestingness for ranking summaries generated from databases. To develop the preliminary foundation for a theory of interestingness within the context of ranking summaries generated from databases. Knowledge Discovery and Measures of Interest is suitable as a secondary text in a graduate level course and as a reference for researchers and practitioners in industry.