Exploratory Causal Analysis With Time Series Data

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Exploratory Causal Analysis With Time Series Data

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Author : James M. McCracken
language : en
Publisher: Springer Nature
Release Date : 2022-06-01

Exploratory Causal Analysis With Time Series Data written by James M. McCracken and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.

Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments. Data analysis techniques are required for identifying causal information and relationships directly from such observational data. This need has led to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal structures in time series data sets and is characterized by a myopic goal to determine which data series from a given set of series might be seen as the primary driver. In this work, ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.

Kaggle Kernels In Action

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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-02-02

Kaggle Kernels In Action written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-02 with Computers categories.

Unlock the power of data science and machine learning with "Kaggle Kernels in Action: From Exploration to Competition." This comprehensive guide offers a structured approach for both beginners and seasoned data enthusiasts, transforming complex concepts into accessible knowledge. Dive deep into the world of Kaggle, the premier platform that bridges learning and application, equipping you with the skills necessary to excel in the dynamic field of data science. Each chapter meticulously addresses critical aspects of the Kaggle experience—from setting up an efficient working environment and mastering data exploration techniques to constructing robust models and tackling real-world challenges. Learn from detailed analyses and case studies that showcase the impact Kaggle has on industries across the globe. This book offers you a roadmap to developing strategies for effective competition engagement and collaboration, ensuring your efforts translate into tangible outcomes. Experience the transformative journey of data science mastery with this indispensable resource. Embrace a learning process enriched by best practices, community engagement, and actionable insights, to hone your analytical prowess and expand your professional horizons. "Kaggle Kernels in Action" not only prepares you for success on Kaggle but empowers you for an enduring career in the evolving landscape of machine learning and data analytics.

Exploiting The Power Of Group Differences

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Author : Guozhu Dong
language : en
Publisher: Springer Nature
Release Date : 2022-05-31

Exploiting The Power Of Group Differences written by Guozhu Dong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.

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.

Mining Structures Of Factual Knowledge From Text

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Author : Xiang Ren
language : en
Publisher: Springer Nature
Release Date : 2022-05-31

Mining Structures Of Factual Knowledge From Text written by Xiang Ren and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.

The real-world data, though massive, is largely unstructured, in the form of natural-language text. It is challenging but highly desirable to mine structures from massive text data, without extensive human annotation and labeling. In this book, we investigate the principles and methodologies of mining structures of factual knowledge (e.g., entities and their relationships) from massive, unstructured text corpora. Departing from many existing structure extraction methods that have heavy reliance on human annotated data for model training, our effort-light approach leverages human-curated facts stored in external knowledge bases as distant supervision and exploits rich data redundancy in large text corpora for context understanding. This effort-light mining approach leads to a series of new principles and powerful methodologies for structuring text corpora, including (1) entity recognition, typing and synonym discovery, (2) entity relation extraction, and (3) open-domain attribute-valuemining and information extraction. This book introduces this new research frontier and points out some promising research directions.

Phrase Mining From Massive Text And Its Applications

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Author : Jialu Liu
language : en
Publisher: Springer Nature
Release Date : 2022-06-01

Phrase Mining From Massive Text And Its Applications written by Jialu Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.

A lot of digital ink has been spilled on "big data" over the past few years. Most of this surge owes its origin to the various types of unstructured data in the wild, among which the proliferation of text-heavy data is particularly overwhelming, attributed to the daily use of web documents, business reviews, news, social posts, etc., by so many people worldwide.A core challenge presents itself: How can one efficiently and effectively turn massive, unstructured text into structured representation so as to further lay the foundation for many other downstream text mining applications? In this book, we investigated one promising paradigm for representing unstructured text, that is, through automatically identifying high-quality phrases from innumerable documents. In contrast to a list of frequent n-grams without proper filtering, users are often more interested in results based on variable-length phrases with certain semantics such as scientific concepts, organizations, slogans, and so on. We propose new principles and powerful methodologies to achieve this goal, from the scenario where a user can provide meaningful guidance to a fully automated setting through distant learning. This book also introduces applications enabled by the mined phrases and points out some promising research directions.

Individual And Collective Graph Mining

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Author : Danai Koutra
language : en
Publisher: Springer Nature
Release Date : 2022-06-01

Individual And Collective Graph Mining written by Danai Koutra and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.

Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, such as an attack on a computer system, disease formation in the human brain, or the fall of a company? This book presents scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. In addition to fast algorithmic methodologies, we also contribute graph-theoretical ideas and models, and real-world applications in two main areas: Individual Graph Mining: We show how to interpretably summarize a single graph by identifying its important graph structures. We complement summarization with inference, which leverages information about few entities (obtained via summarization or other methods) and the network structure to efficiently and effectively learn information about the unknown entities. Collective Graph Mining: We extend the idea of individual-graph summarization to time-evolving graphs, and show how to scalably discover temporal patterns. Apart from summarization, we claim that graph similarity is often the underlying problem in a host of applications where multiple graphs occur (e.g., temporal anomaly detection, discovery of behavioral patterns), and we present principled, scalable algorithms for aligning networks and measuring their similarity. The methods that we present in this book leverage techniques from diverse areas, such as matrix algebra, graph theory, optimization, information theory, machine learning, finance, and social science,to solve real-world problems. We present applications of our exploration algorithms to massive datasets, including a Web graph of 6.6 billion edges, a Twitter graph of 1.8 billion edges, brain graphs with up to 90 million edges, collaboration, peer-to-peer networks, browser logs, all spanning millions of users and interactions.

Methods In Brain Connectivity Inference Through Multivariate Time Series Analysis

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Author : Koichi Sameshima
language : en
Publisher: CRC Press
Release Date : 2014-03-21

Methods In Brain Connectivity Inference Through Multivariate Time Series Analysis written by Koichi Sameshima and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-03-21 with Mathematics categories.

Interest in brain connectivity inference has become ubiquitous and is now increasingly adopted in experimental investigations of clinical, behavioral, and experimental neurosciences. Methods in Brain Connectivity Inference through Multivariate Time Series Analysis gathers the contributions of leading international authors who discuss different time series analysis approaches, providing a thorough survey of information on how brain areas effectively interact. Incorporating multidisciplinary work in applied mathematics, statistics, and animal and human experiments at the forefront of the field, the book addresses the use of time series data in brain connectivity interference studies. Contributors present codes and data examples to back up their methodological descriptions, exploring the details of each proposed method as well as an appreciation of their merits and limitations. Supplemental material for the book, including code, data, practical examples, and color figures is supplied in the form of a CD with directories organized by chapter and instruction files that provide additional detail. The field of brain connectivity inference is growing at a fast pace with new data/signal processing proposals emerging so often as to make it difficult to be fully up to date. This consolidated panorama of data-driven methods includes theoretical bases allied to computational tools, offering readers immediate hands-on experience in this dynamic arena.

Multidimensional Mining Of Massive Text Data

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Author : Chao Zhang
language : en
Publisher: Springer Nature
Release Date : 2022-06-01

Multidimensional Mining Of Massive Text Data written by Chao Zhang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.

Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional—they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task. This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making. The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.

Computer Science Engineering

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Author : Gururaj H L
language : en
Publisher: CRC Press
Release Date : 2024-12-20

Computer Science Engineering written by Gururaj H L and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-20 with Computers categories.

This book provides a comprehensive overview of the latest advancements and research in the fields of computing and intelligent information systems. It compiles cutting-edge studies, innovative methodologies, and practical applications presented at the conference ICCIIS 2024. The book delves into several core areas of modern computing and intelligent information systems. Key topics include artificial intelligence, exploring machine learning algorithms and neural networks; information systems and robotic process automation, highlighting efficient business process automation strategies; and signal, image, and video processing, focusing on innovative techniques for multimedia analysis. Big data analytics is also covered with insights into data mining and predictive analytics. Cloud computing and cybersecurity are explored, emphasizing secure, scalable solutions for data storage and protection. The Internet of Things (IoT) is examined for its impact on interconnected devices and smart systems. Additionally, the book explores advanced computing and intelligent networks, addressing the development of high-performance computing systems and sophisticated network architectures. This book is intended for academics, researchers, and professionals in the fields of computing and information systems, as well as students pursuing advanced studies in these areas. It is also a valuable resource for industry practitioners seeking to stay abreast of the latest trends and innovations in AI, big data, and cybersecurity.

Structural Equation Models

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Author : J. Christopher Westland
language : en
Publisher: Springer
Release Date : 2015-04-25

Structural Equation Models written by J. Christopher Westland and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-25 with Technology & Engineering categories.

This compact reference surveys the full range of available structural equation modeling (SEM) methodologies. It reviews applications in a broad range of disciplines, particularly in the social sciences where many key concepts are not directly observable. This is the first book to present SEM’s development in its proper historical context–essential to understanding the application, strengths and weaknesses of each particular method. This book also surveys the emerging path and network approaches that complement and enhance SEM, and that will grow importance in the near future. SEM’s ability to accommodate unobservable theory constructs through latent variables is of significant importance to social scientists. Latent variable theory and application are comprehensively explained and methods are presented for extending their power, including guidelines for data preparation, sample size calculation and the special treatment of Likert scale data. Tables of software, methodologies and fit statistics provide a concise reference for any research program, helping assure that its conclusions are defensible and publishable.