Mining Latent Entity Structures

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Mining Latent Entity Structures

Author : Chi Wang
Publisher : Springer Nature
ISBN 13 : 3031019075
Total Pages : 147 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Mining Latent Entity Structures by : Chi Wang

Download or read book Mining Latent Entity Structures written by Chi Wang and published by Springer Nature. This book was released on 2022-05-31 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: The "big data" era is characterized by an explosion of information in the form of digital data collections, ranging from scientific knowledge, to social media, news, and everyone's daily life. Examples of such collections include scientific publications, enterprise logs, news articles, social media, and general web pages. Valuable knowledge about multi-typed entities is often hidden in the unstructured or loosely structured, interconnected data. Mining latent structures around entities uncovers hidden knowledge such as implicit topics, phrases, entity roles and relationships. In this monograph, we investigate the principles and methodologies of mining latent entity structures from massive unstructured and interconnected data. We propose a text-rich information network model for modeling data in many different domains. This leads to a series of new principles and powerful methodologies for mining latent structures, including (1) latent topical hierarchy, (2) quality topical phrases, (3) entity roles in hierarchical topical communities, and (4) entity relations. This book also introduces applications enabled by the mined structures and points out some promising research directions.

Mining Structures of Factual Knowledge from Text

Author : Xiang Ren
Publisher : Springer Nature
ISBN 13 : 3031019121
Total Pages : 183 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Mining Structures of Factual Knowledge from Text by : Xiang Ren

Download or read book Mining Structures of Factual Knowledge from Text written by Xiang Ren and published by Springer Nature. This book was released on 2022-05-31 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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-value mining and information extraction. This book introduces this new research frontier and points out some promising research directions.

Individual and Collective Graph Mining

Author : Danai Koutra
Publisher : Springer Nature
ISBN 13 : 3031019113
Total Pages : 197 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Individual and Collective Graph Mining by : Danai Koutra

Download or read book Individual and Collective Graph Mining written by Danai Koutra and published by Springer Nature. This book was released on 2022-06-01 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Phrase Mining from Massive Text and Its Applications

Author : Jialu Liu
Publisher : Springer Nature
ISBN 13 : 3031019105
Total Pages : 79 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Phrase Mining from Massive Text and Its Applications by : Jialu Liu

Download or read book Phrase Mining from Massive Text and Its Applications written by Jialu Liu and published by Springer Nature. This book was released on 2022-06-01 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Mining Human Mobility in Location-Based Social Networks

Author : Huiji Gao
Publisher : Springer Nature
ISBN 13 : 3031019083
Total Pages : 99 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Mining Human Mobility in Location-Based Social Networks by : Huiji Gao

Download or read book Mining Human Mobility in Location-Based Social Networks written by Huiji Gao and published by Springer Nature. This book was released on 2022-06-01 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook Places, which have attracted an increasing number of users and greatly enriched their urban experience. Typical location-based social networking sites allow a user to "check in" at a real-world POI (point of interest, e.g., a hotel, restaurant, theater, etc.), leave tips toward the POI, and share the check-in with their online friends. The check-in action bridges the gap between real world and online social networks, resulting in a new type of social networks, namely location-based social networks (LBSNs). Compared to traditional GPS data, location-based social networks data contains unique properties with abundant heterogeneous information to reveal human mobility, i.e., "when and where a user (who) has been to for what," corresponding to an unprecedented opportunity to better understand human mobility from spatial, temporal, social, and content aspects. The mining and understanding of human mobility can further lead to effective approaches to improve current location-based services from mobile marketing to recommender systems, providing users more convenient life experience than before. This book takes a data mining perspective to offer an overview of studying human mobility in location-based social networks and illuminate a wide range of related computational tasks. It introduces basic concepts, elaborates associated challenges, reviews state-of-the-art algorithms with illustrative examples and real-world LBSN datasets, and discusses effective evaluation methods in mining human mobility. In particular, we illustrate unique characteristics and research opportunities of LBSN data, present representative tasks of mining human mobility on location-based social networks, including capturing user mobility patterns to understand when and where a user commonly goes (location prediction), and exploiting user preferences and location profiles to investigate where and when a user wants to explore (location recommendation), along with studying a user's check-in activity in terms of why a user goes to a certain location.

Multidimensional Mining of Massive Text Data

Author : Chao Zhang
Publisher : Springer Nature
ISBN 13 : 3031019148
Total Pages : 183 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Multidimensional Mining of Massive Text Data by : Chao Zhang

Download or read book Multidimensional Mining of Massive Text Data written by Chao Zhang and published by Springer Nature. This book was released on 2022-06-01 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Exploratory Causal Analysis with Time Series Data

Author : James M. McCracken
Publisher : Springer Nature
ISBN 13 : 3031019091
Total Pages : 133 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Exploratory Causal Analysis with Time Series Data by : James M. McCracken

Download or read book Exploratory Causal Analysis with Time Series Data written by James M. McCracken and published by Springer Nature. This book was released on 2022-06-01 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Correlation Clustering

Author : Bonchi Francesco
Publisher : Springer Nature
ISBN 13 : 3031792106
Total Pages : 133 pages
Book Rating : 4.0/5 (317 download)

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Book Synopsis Correlation Clustering by : Bonchi Francesco

Download or read book Correlation Clustering written by Bonchi Francesco and published by Springer Nature. This book was released on 2022-05-31 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given a set of objects and a pairwise similarity measure between them, the goal of correlation clustering is to partition the objects in a set of clusters to maximize the similarity of the objects within the same cluster and minimize the similarity of the objects in different clusters. In most of the variants of correlation clustering, the number of clusters is not a given parameter; instead, the optimal number of clusters is automatically determined. Correlation clustering is perhaps the most natural formulation of clustering: as it just needs a definition of similarity, its broad generality makes it applicable to a wide range of problems in different contexts, and, particularly, makes it naturally suitable to clustering structured objects for which feature vectors can be difficult to obtain. Despite its simplicity, generality, and wide applicability, correlation clustering has so far received much more attention from an algorithmic-theory perspective than from the data-mining community. The goal of this lecture is to show how correlation clustering can be a powerful addition to the toolkit of a data-mining researcher and practitioner, and to encourage further research in the area.

Detecting Fake News on Social Media

Author : Kai Shu
Publisher : Springer Nature
ISBN 13 : 3031019156
Total Pages : 121 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Detecting Fake News on Social Media by : Kai Shu

Download or read book Detecting Fake News on Social Media written by Kai Shu and published by Springer Nature. This book was released on 2022-05-31 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past decade, social media has become increasingly popular for news consumption due to its easy access, fast dissemination, and low cost. However, social media also enables the wide propagation of "fake news," i.e., news with intentionally false information. Fake news on social media can have significant negative societal effects. Therefore, fake news detection on social media has recently become an emerging research area that is attracting tremendous attention. This book, from a data mining perspective, introduces the basic concepts and characteristics of fake news across disciplines, reviews representative fake news detection methods in a principled way, and illustrates challenging issues of fake news detection on social media. In particular, we discussed the value of news content and social context, and important extensions to handle early detection, weakly-supervised detection, and explainable detection. The concepts, algorithms, and methods described in this lecture can help harness the power of social media to build effective and intelligent fake news detection systems. This book is an accessible introduction to the study of detecting fake news on social media. It is an essential reading for students, researchers, and practitioners to understand, manage, and excel in this area. This book is supported by additional materials, including lecture slides, the complete set of figures, key references, datasets, tools used in this book, and the source code of representative algorithms. The readers are encouraged to visit the book website for the latest information: http://dmml.asu.edu/dfn/

Exploiting the Power of Group Differences

Author : Guozhu Dong
Publisher : Springer Nature
ISBN 13 : 303101913X
Total Pages : 135 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Exploiting the Power of Group Differences by : Guozhu Dong

Download or read book Exploiting the Power of Group Differences written by Guozhu Dong and published by Springer Nature. This book was released on 2022-05-31 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Advances in Knowledge Discovery and Data Mining

Author : Kamal Karlapalem
Publisher : Springer Nature
ISBN 13 : 303075765X
Total Pages : 794 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Advances in Knowledge Discovery and Data Mining by : Kamal Karlapalem

Download or read book Advances in Knowledge Discovery and Data Mining written by Kamal Karlapalem and published by Springer Nature. This book was released on 2021-05-07 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021. The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data.

DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

Author : Mr. Srinivas Rao Adabala
Publisher : Xoffencerpublication
ISBN 13 : 8119534174
Total Pages : 207 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION by : Mr. Srinivas Rao Adabala

Download or read book DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION written by Mr. Srinivas Rao Adabala and published by Xoffencerpublication. This book was released on 2023-08-14 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has developed as a useful approach for data mining tasks such as unsupervised feature learning and representation. This is thanks to its ability to learn from examples with no prior guidance. Unsupervised learning is the process of discovering patterns and structures in unlabeled data without the use of any explicit labels or annotations. This type of learning does not require the data to be annotated or labelled. This is especially helpful in situations in which labelled data are few or nonexistent. Unsupervised feature learning and representation have seen widespread application of deep learning methods such as auto encoders and generative adversarial networks (GANs). These algorithms learn to describe the data in a hierarchical fashion, where higher-level characteristics are stacked upon lower-level ones, capturing increasingly complicated and abstract patterns as they progress. Neural networks are known as Auto encoders, and they are designed to reconstruct their input data from a compressed representation known as the latent space. The hidden layers of the network are able to learn to encode valuable characteristics that capture the underlying structure of the data when an auto encoder is trained on input that does not have labels attached to it. It is possible to use the reconstruction error as a measurement of how well the auto encoder has learned to represent the data. GANs are made up of two different types of networks: a generator network and a discriminator network. While the discriminator network is taught to differentiate between real and synthetic data, the generator network is taught to generate synthetic data samples that are an accurate representation of the real data. By going through an adversarial training process, both the generator and the discriminator are able to improve their skills. The generator is able to produce more realistic samples, and the discriminator is better able to tell the difference between real and fake samples. One meaningful representation of the data could be understood as being contained within the latent space of the generator. After the deep learning model has learned a reliable representation of the data, it can be put to use for a variety of data mining activities.

Data Engineering

Author : Yupo Chan
Publisher : Springer Science & Business Media
ISBN 13 : 1441901760
Total Pages : 381 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Data Engineering by : Yupo Chan

Download or read book Data Engineering written by Yupo Chan and published by Springer Science & Business Media. This book was released on 2009-10-15 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: DATA ENGINEERING: Mining, Information, and Intelligence describes applied research aimed at the task of collecting data and distilling useful information from that data. Most of the work presented emanates from research completed through collaborations between Acxiom Corporation and its academic research partners under the aegis of the Acxiom Laboratory for Applied Research (ALAR). Chapters are roughly ordered to follow the logical sequence of the transformation of data from raw input data streams to refined information. Four discrete sections cover Data Integration and Information Quality; Grid Computing; Data Mining; and Visualization. Additionally, there are exercises at the end of each chapter. The primary audience for this book is the broad base of anyone interested in data engineering, whether from academia, market research firms, or business-intelligence companies. The volume is ideally suited for researchers, practitioners, and postgraduate students alike. With its focus on problems arising from industry rather than a basic research perspective, combined with its intelligent organization, extensive references, and subject and author indices, it can serve the academic, research, and industrial audiences.

Collaborative Filtering Using Data Mining and Analysis

Author : Bhatnagar, Vishal
Publisher : IGI Global
ISBN 13 : 1522504907
Total Pages : 336 pages
Book Rating : 4.5/5 (225 download)

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Book Synopsis Collaborative Filtering Using Data Mining and Analysis by : Bhatnagar, Vishal

Download or read book Collaborative Filtering Using Data Mining and Analysis written by Bhatnagar, Vishal and published by IGI Global. This book was released on 2016-07-13 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Internet usage has become a normal and essential aspect of everyday life. Due to the immense amount of information available on the web, it has become obligatory to find ways to sift through and categorize the overload of data while removing redundant material. Collaborative Filtering Using Data Mining and Analysis evaluates the latest patterns and trending topics in the utilization of data mining tools and filtering practices. Featuring emergent research and optimization techniques in the areas of opinion mining, text mining, and sentiment analysis, as well as their various applications, this book is an essential reference source for researchers and engineers interested in collaborative filtering.

Network Role Mining and Analysis

Author : Derek Doran
Publisher : Springer
ISBN 13 : 3319538861
Total Pages : 109 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Network Role Mining and Analysis by : Derek Doran

Download or read book Network Role Mining and Analysis written by Derek Doran and published by Springer. This book was released on 2017-03-20 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: This brief presents readers with a summary of classic, modern, and state-of-the-art methods for discovering the roles of entities in networks (including social networks) that range from small to large-scale. It classifies methods by their mathematical underpinning, whether they are driven by implications about entity behaviors in system, or if they are purely data driven. The brief also discusses when and how each method should be applied, and discusses some outstanding challenges toward the development of future role mining methods of each type.

Advances in Knowledge Discovery and Data Mining

Author : João Gama
Publisher : Springer Nature
ISBN 13 : 3031059816
Total Pages : 396 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Advances in Knowledge Discovery and Data Mining by : João Gama

Download or read book Advances in Knowledge Discovery and Data Mining written by João Gama and published by Springer Nature. This book was released on 2022-05-09 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 3-volume set LNAI 13280, LNAI 13281 and LNAI 13282 constitutes the proceedings of the 26th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2022, which was held during May 2022 in Chengdu, China. The 121 papers included in the proceedings were carefully reviewed and selected from a total of 558 submissions. They were organized in topical sections as follows: Part I: Data Science and Big Data Technologies, Part II: Foundations; and Part III: Applications.

Probabilistic Approaches for Social Media Analysis

Author : Kun Yue
Publisher :
ISBN 13 : 9811207380
Total Pages : 290 pages
Book Rating : 4.8/5 (112 download)

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Book Synopsis Probabilistic Approaches for Social Media Analysis by : Kun Yue

Download or read book Probabilistic Approaches for Social Media Analysis written by Kun Yue and published by . This book was released on 2020 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This unique compendium focuses on the acquisition and analysis of social media data. The approaches concern both the data-intensive characteristics and graphical structures of social media. The book addresses the critical problems in social media analysis, which representatively cover its lifecycle. The must-have volume is an excellent reference text for professionals, researchers, academics and graduate students in AI and databases"--Publisher's website.