Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis Problems, Dong Guozhu

Автор: Dong Guozhu
Название: Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis Problems
ISBN: 1681735040 ISBN-13(EAN): 9781681735047
Издательство: Mare Nostrum (Eurospan)
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Цена: 77610.00 T
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Описание: This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included. Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on. EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines. Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest. We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.

Автор: Pan Jeff Z., Vetere Guido, Gomez-Perez Jose Manuel
Название: Exploiting Linked Data and Knowledge Graphs in Large Organisations
ISBN: 3319833391 ISBN-13(EAN): 9783319833392
Издательство: Springer
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Цена: 158380.00 T
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Part I Knowledge Graph Foundations & Architecture.- Part II Constructing, Understanding and Consuming Knowledge Graphs.- Part III Industrial Applications and Successful Stories.

Автор: Jeff Z. Pan; Guido Vetere; Jose Manuel Gomez-Perez
Название: Exploiting Linked Data and Knowledge Graphs for Large Organisations
ISBN: 3319456520 ISBN-13(EAN): 9783319456522
Издательство: Springer
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Цена: 121110.00 T
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Описание: This book addresses the topic of exploiting enterprise-linked data with a particular focus on knowledge construction and accessibility within enterprises. It identifies the gaps between the requirements of enterprise knowledge consumption and "standard" data consuming technologies by analysing real-world use cases, and proposes the enterprise knowledge graph to fill such gaps.

It provides concrete guidelines for effectively deploying linked-data graphs within and across business organizations. It is divided into three parts, focusing on the key technologies for constructing, understanding and employing knowledge graphs. Part 1 introduces basic background information and technologies, and presents a simple architecture to elucidate the main phases and tasks required during the lifecycle of knowledge graphs. Part 2 focuses on technical aspects; it starts with state-of-the art knowledge-graph construction approaches, and then discusses exploration and exploitation techniques as well as advanced question-answering topics concerning knowledge graphs. Lastly, Part 3 demonstrates examples of successful knowledge graph applications in the media industry, healthcare and cultural heritage, and offers conclusions and future visions.