Pattern Detection and Discovery
Author | : David Hand |
Publisher | : |
Total Pages | : 244 |
Release | : 2014-01-15 |
Genre | : |
ISBN | : 9783662199459 |
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Author | : David Hand |
Publisher | : |
Total Pages | : 244 |
Release | : 2014-01-15 |
Genre | : |
ISBN | : 9783662199459 |
Author | : David J. Hand |
Publisher | : |
Total Pages | : 226 |
Release | : 2002 |
Genre | : |
ISBN | : |
Author | : David J Hand |
Publisher | : Springer |
Total Pages | : 239 |
Release | : 2003-08-02 |
Genre | : Computers |
ISBN | : 3540457283 |
The collation of large electronic databases of scienti?c and commercial infor- tion has led to a dramatic growth of interest in methods for discovering struc- res in such databases. These methods often go under the general name of data mining. One important subdiscipline within data mining is concerned with the identi?cation and detection of anomalous, interesting, unusual, or valuable - cords or groups of records, which we call patterns. Familiar examples are the detection of fraud in credit-card transactions, of particular coincident purchases in supermarket transactions, of important nucleotide sequences in gene sequence analysis, and of characteristic traces in EEG records. Tools for the detection of such patterns have been developed within the data mining community, but also within other research communities, typically without an awareness that the - sic problem was common to many disciplines. This is not unreasonable: each of these disciplines has a large literature of its own, and a literature which is growing rapidly. Keeping up with any one of these is di?cult enough, let alone keeping up with others as well, which may in any case be couched in an - familiar technical language. But, of course, this means that opportunities are being lost, discoveries relating to the common problem made in one area are not transferred to the other area, and breakthroughs and problem solutions are being rediscovered, or not discovered for a long time, meaning that e?ort is being wasted and opportunities may be lost.
Author | : Pradeep Kumar |
Publisher | : |
Total Pages | : 272 |
Release | : 2011-07-01 |
Genre | : Sequential pattern mining |
ISBN | : 9781613500583 |
"This book provides a comprehensive view of sequence mining techniques, and present current research and case studies in Pattern Discovery in Sequential data authored by researchers and practitioners"--
Author | : Sankar K. Pal |
Publisher | : CRC Press |
Total Pages | : 275 |
Release | : 2004-05-27 |
Genre | : Computers |
ISBN | : 1135436401 |
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, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks. Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.
Author | : Barzan Mozafari |
Publisher | : |
Total Pages | : 450 |
Release | : 2011 |
Genre | : |
ISBN | : |
Author | : Hongxing Wang |
Publisher | : Springer |
Total Pages | : 93 |
Release | : 2017-06-14 |
Genre | : Computers |
ISBN | : 9811048401 |
This book presents a systematic study of visual pattern discovery, from unsupervised to semi-supervised manner approaches, and from dealing with a single feature to multiple types of features. Furthermore, it discusses the potential applications of discovering visual patterns for visual data analytics, including visual search, object and scene recognition. It is intended as a reference book for advanced undergraduates or postgraduate students who are interested in visual data analytics, enabling them to quickly access the research world and acquire a systematic methodology rather than a few isolated techniques to analyze visual data with large variations. It is also inspiring for researchers working in computer vision and pattern recognition fields. Basic knowledge of linear algebra, computer vision and pattern recognition would be helpful to readers.
Author | : David J Hand |
Publisher | : Springer |
Total Pages | : 232 |
Release | : 2002-09-04 |
Genre | : Computers |
ISBN | : 9783540441489 |
The collation of large electronic databases of scienti?c and commercial infor- tion has led to a dramatic growth of interest in methods for discovering struc- res in such databases. These methods often go under the general name of data mining. One important subdiscipline within data mining is concerned with the identi?cation and detection of anomalous, interesting, unusual, or valuable - cords or groups of records, which we call patterns. Familiar examples are the detection of fraud in credit-card transactions, of particular coincident purchases in supermarket transactions, of important nucleotide sequences in gene sequence analysis, and of characteristic traces in EEG records. Tools for the detection of such patterns have been developed within the data mining community, but also within other research communities, typically without an awareness that the - sic problem was common to many disciplines. This is not unreasonable: each of these disciplines has a large literature of its own, and a literature which is growing rapidly. Keeping up with any one of these is di?cult enough, let alone keeping up with others as well, which may in any case be couched in an - familiar technical language. But, of course, this means that opportunities are being lost, discoveries relating to the common problem made in one area are not transferred to the other area, and breakthroughs and problem solutions are being rediscovered, or not discovered for a long time, meaning that e?ort is being wasted and opportunities may be lost.
Author | : Animesh Adhikari |
Publisher | : Springer Science & Business Media |
Total Pages | : 247 |
Release | : 2013-12-09 |
Genre | : Technology & Engineering |
ISBN | : 3319034103 |
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.
Author | : Mark R. Anderson |
Publisher | : FiReBooks |
Total Pages | : 240 |
Release | : 2017-11-20 |
Genre | : |
ISBN | : 9780996725446 |
Renowned technology and economics forecaster Mark Anderson reveals hidden patterns beneath the art and science of predicting the future. Through a series of personal vignettes, Anderson exposes a complex web of causes, influences, and effects that propel today's world, then describes strategies that he employs to lay bare new trends, to make new discoveries in a wide variety of disciplines, and to accurately foresee future events.