Advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely. This 2006 book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique's potential.

Research Methods for the Biosciences is the perfect resource for students wishing to develop the crucial skills needed for designing, carrying out, and reporting research, with examples throughout the text drawn from real undergraduate projects.

This book comprehensively covers the topic of data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three sections: The first section is an introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics. Followed by discussion on wide range of applications of data science and widely used techniques in data science. The second section is devoted to the tools and techniques of data science. It consists of data pre-processing, feature selection, classification and clustering concepts as well as an introduction to text mining and opining mining. And finally, the third section of the book focuses on two programming languages commonly used for data science projects i.e. Python and R programming language. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. The book is suitable for both undergraduate and postgraduate students as well as those carrying out research in data science. It can be used as a textbook for undergraduate students in computer science, engineering and mathematics. It can also be accessible to undergraduate students from other areas with the adequate background. The more advanced chapters can be used by postgraduate researchers intending to gather a deeper theoretical understanding.

This monograph is multivariate, multi-perspective and multipurpose. We intend to be innovatively integrative through statistical synthesis. Innovation requires capacity to operate in ways that are not ordinary, which means that conventional computations and generic graphics will not meet the needs of an adaptive approach. Flexible formulation and special schematics are essential elements that must be manageable and economical.

Aquatic creatures.

A hands-on guide to making valuable decisions from data using advanced data mining methods and techniques This second installment in the Making Sense of Data series continues to explore a diverse range of commonly used approaches to making and communicating decisions from data. Delving into more technical topics, this book equips readers with advanced data mining methods that are needed to successfully translate raw data into smart decisions across various fields of research including business, engineering, finance, and the social sciences. Following a comprehensive introduction that details how to define a problem, perform an analysis, and deploy the results, Making Sense of Data II addresses the following key techniques for advanced data analysis: Data Visualization reviews principles and methods for understanding and communicating data through the use of visualization including single variables, the relationship between two or more variables, groupings in data, and dynamic approaches to interacting with data through graphical user interfaces. Clustering outlines common approaches to clustering data sets and provides detailed explanations of methods for determining the distance between observations and procedures for clustering observations. Agglomerative hierarchical clustering, partitioned-based clustering, and fuzzy clustering are also discussed. Predictive Analytics presents a discussion on how to build and assess models, along with a series of predictive analytics that can be used in a variety of situations including principal component analysis, multiple linear regression, discriminate analysis, logistic regression, and Naïve Bayes. Applications demonstrates the current uses of data mining across a wide range of industries and features case studies that illustrate the related applications in real-world scenarios. Each method is discussed within the context of a data mining process including defining the problem and deploying the results, and readers are provided with guidance on when and how each method should be used. The related Web site for the series (www.makingsenseofdata.com) provides a hands-on data analysis and data mining experience. Readers wishing to gain more practical experience will benefit from the tutorial section of the book in conjunction with the TraceisTM software, which is freely available online. With its comprehensive collection of advanced data mining methods coupled with tutorials for applications in a range of fields, Making Sense of Data II is an indispensable book for courses on data analysis and data mining at the upper-undergraduate and graduate levels. It also serves as a valuable reference for researchers and professionals who are interested in learning how to accomplish effective decision making from data and understanding if data analysis and data mining methods could help their organization.