Pattern Discovery
Author: Douglas Danner
Publisher:
Published: 1995
Total Pages:
ISBN-13:
DOWNLOAD EBOOKRead and Download eBook Full
Author: Douglas Danner
Publisher:
Published: 1995
Total Pages:
ISBN-13:
DOWNLOAD EBOOKAuthor: Laxmi Parida
Publisher: CRC Press
Published: 2007-07-04
Total Pages: 512
ISBN-13: 1420010735
DOWNLOAD EBOOKThe computational methods of bioinformatics are being used more and more to process the large volume of current biological data. Promoting an understanding of the underlying biology that produces this data, Pattern Discovery in Bioinformatics: Theory and Algorithms provides the tools to study regularities in biological data. Taking a systema
Author: Zheng Rong Yang
Publisher: World Scientific
Published: 2021-09-17
Total Pages: 462
ISBN-13: 9811240132
DOWNLOAD EBOOKThis book provides the research directions for new or junior researchers who are going to use machine learning approaches for biological pattern discovery. The book was written based on the research experience of the author's several research projects in collaboration with biologists worldwide. The chapters are organised to address individual biological pattern discovery problems. For each subject, the research methodologies and the machine learning algorithms which can be employed are introduced and compared. Importantly, each chapter was written with the aim to help the readers to transfer their knowledge in theory to practical implementation smoothly. Therefore, the R programming environment was used for each subject in the chapters. The author hopes that this book can inspire new or junior researchers' interest in biological pattern discovery using machine learning algorithms.
Author: Norwood Russell Hanson
Publisher: CUP Archive
Published: 1979
Total Pages: 260
ISBN-13:
DOWNLOAD EBOOKAuthor: Jason T. L. Wang
Publisher: Oxford University Press
Published: 1999
Total Pages: 272
ISBN-13: 0195119401
DOWNLOAD EBOOKFinding patterns in biomolecular data, particularly in DNA and RNA, is at the center of modern biological research. These data are complex and growing rapidly, so the search for patterns requires increasingly sophisticated computer methods. Pattern Discovery in Biomolecular Data provides a clear, up-to-date summary of the principal techniques. Each chapter is self-contained, and the techniques are drawn from many fields, including graph theory, information theory, statistics, genetic algorithms, computer visualization, and vision. Since pattern searches often benefit from multiple approaches, the book presents methods in their purest form so that readers can best choose the method or combination that fits their needs. The chapters focus on finding patterns in DNA, RNA, and protein sequences, finding patterns in 2D and 3D structures, and choosing system components. This volume will be invaluable for all workers in genomics and genetic analysis, and others whose research requires biocomputing.
Author: Yan-Ping Huang
Publisher: 黃燕萍工作室
Published: 2014-07-25
Total Pages: 73
ISBN-13:
DOWNLOAD EBOOKData mining, a recent and contemporary research topic, is the process of automatically searching large volumes of data for patterns in computing. Nowadays, pattern discovery is a field within the area of data mining. In general, large volumes of time series data are contained in financial database and these data have some useful patterns which could not be found easily. Many financial studies in time series data analysis use linear regression model to estimate the variation and trend of the data. However, traditional methods of time series analysis used special types or linear models to describe the data. Linear models can achieve high accuracy when linear variation of the data is small, however, if the variation range exceeds a certain limit, the linear models has a lower performance in estimated accuracy. Among these traditional methods, SOM (Self Organizing Map) is a well-known non-linear model to extract pattern with numeric data. Many researches extract pattern from numeric data attributes rather than categorical or mixed data. It does not extract the major values from pattern rules, either. The purpose of this study is to provide a novel architecture in mining patterns from mixed data that uses a systematic approach in the financial database information mining, and try to find the patterns for estimate the trend or for special event’s occurrence. This present study employs ESA algorithm which integrates both EViSOM algorithm and EAOI algorithm. EViSOM algorithm is used to calculate the distance between the categorical and numeric data for pattern finding, whereas EAOI algorithm serves to generalize major values using conceptual hierarchies for patterns and major values extraction in financial database. The attempt of using ESA algorithm in this study is to discover the pattern in the Concept Hierarchy based Pattern Discovery (CHPD) architecture. Specifically, this architecture facilitates the direct handling of mixed data, including categorical and numeric values. This mining architecture is able to simulate human intelligence and discover patterns automatically, and it also demonstrates knowledge pattern discovery and rule extraction.
Author: Fabio Fassetti
Publisher: Springer
Published: 2017-09-01
Total Pages: 51
ISBN-13: 3319634771
DOWNLOAD EBOOKThis work provides a review of biological networks as a model for analysis, presenting and discussing a number of illuminating analyses. Biological networks are an effective model for providing insights about biological mechanisms. Networks with different characteristics are employed for representing different scenarios. This powerful model allows analysts to perform many kinds of analyses which can be mined to provide interesting information about underlying biological behaviors. The text also covers techniques for discovering exceptional patterns, such as a pattern accounting for local similarities and also collaborative effects involving interactions between multiple actors (for example genes). Among these exceptional patterns, of particular interest are discriminative patterns, namely those which are able to discriminate between two input populations (for example healthy/unhealthy samples). In addition, the work includes a discussion on the most recent proposal on discovering discriminative patterns, in which there is a labeled network for each sample, resulting in a database of networks representing a sample set. This enables the analyst to achieve a much finer analysis than with traditional techniques, which are only able to consider an aggregated network of each population.
Author: Hongxing Wang
Publisher: Springer
Published: 2017-06-14
Total Pages: 93
ISBN-13: 9811048401
DOWNLOAD EBOOKThis 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: Pradeep Kumar
Publisher:
Published: 2011-07-01
Total Pages: 272
ISBN-13: 9781613500583
DOWNLOAD EBOOK"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: Mark R. Anderson
Publisher: FiReBooks
Published: 2017-11-20
Total Pages: 240
ISBN-13: 9780996725446
DOWNLOAD EBOOKRenowned 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.