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Clustering Guided Multi Label Text Classification
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Book Synopsis Clustering Guided Multi-label Text Classification by : Mohammad Salim Ahmed
Download or read book Clustering Guided Multi-label Text Classification written by Mohammad Salim Ahmed and published by . This book was released on 2012 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent of social networking and mobile computing, there has been an enormous increase in the amount of data over the Internet. And, a significant portion of this data is in text form. That being the case, an effective automated system needs to be developed that can perform classification of text data so that knowledge can be efficiently extracted from the data. However, text data is usually multi-label in nature. In other words, a single text document may be associated with multiple class labels simultaneously. Here, we propose a solution to the problem of multi-label text classification. There are a number of challenges when classifying multi-label text data. For example, to determine how many labels should be associated with a particular text data instance. Another challenge associated with text data is its high and sparse dimensionality. Finally, we also need to consider the highly shared feature space across multiple class labels that makes it difficult to find features that are specific to any single class label.
Book Synopsis Multi-label Classification and Clustering for Acoustics and Computer Security by : Andreas Peter Streich
Download or read book Multi-label Classification and Clustering for Acoustics and Computer Security written by Andreas Peter Streich and published by . This book was released on 2010 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multilabel Classification by : Francisco Herrera
Download or read book Multilabel Classification written by Francisco Herrera and published by Springer. This book was released on 2016-08-09 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: • The special characteristics of multi-labeled data and the metrics available to measure them.• The importance of taking advantage of label correlations to improve the results.• The different approaches followed to face multi-label classification.• The preprocessing techniques applicable to multi-label datasets.• The available software tools to work with multi-label data. This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.
Download or read book FFIT 2022 written by Holger Haldenwang and published by European Alliance for Innovation. This book was released on 2023-04-14 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 2022 International Conference on Financial Innovation, FinTech and Information Technology (FFIT 2022), hosted by Shenzhen University of Technology and organized by the Financial Innovation and Fintech Research Center of Shenzhen University of Technology, was held on October 28-30, 2022 in Shenzhen, China. Due to the current COVID-19 pandemic and the strict travelling rules, it is still difficult to take international travel for all our attendees to participate in the conference. Therefore, FFIT 2022 was held as a hybrid event. FFIT 2022 brought together innovative academics and industrial experts in the field of Financial Innovation, Financial Technology and Information Technology to discuss the latest research results in this field.
Book Synopsis Predictive Clustering by : Hendrik Blockeel
Download or read book Predictive Clustering written by Hendrik Blockeel and published by Springer. This book was released on 2012-05-31 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a novel paradigm for machine learning and data mining called predictive clustering, which covers a broad variety of learning tasks and offers a fresh perspective on existing techniques. The book presents an informal introduction to predictive clustering, describing learning tasks and settings, and then continues with a formal description of the paradigm, explaining algorithms for learning predictive clustering trees and predictive clustering rules, as well as presenting the applicability of these learning techniques to a broad range of tasks. Variants of decision tree learning algorithms are also introduced. Finally, the book offers several significant applications in ecology and bio-informatics. The book is written in a straightforward and easy-to-understand manner, aimed at varied readership, ranging from researchers with an interest in machine learning techniques to practitioners of data mining technology in the areas of ecology and bioinformatics.
Book Synopsis Natural Scientific Language Processing and Research Knowledge Graphs by : Georg Rehm
Download or read book Natural Scientific Language Processing and Research Knowledge Graphs written by Georg Rehm and published by Springer Nature. This book was released on with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advanced Data Mining and Applications by : Xiaochun Yang
Download or read book Advanced Data Mining and Applications written by Xiaochun Yang and published by Springer Nature. This book was released on 2023-12-06 with total page 717 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023. The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.
Book Synopsis Advances in Information Retrieval by : Joemon M. Jose
Download or read book Advances in Information Retrieval written by Joemon M. Jose and published by Springer Nature. This book was released on 2020-04-11 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 12035 and 12036 constitutes the refereed proceedings of the 42nd European Conference on IR Research, ECIR 2020, held in Lisbon, Portugal, in April 2020.* The 55 full papers presented together with 8 reproducibility papers, 46 short papers, 10 demonstration papers, 12 invited CLEF papers, 7 doctoral consortium papers, 4 workshop papers, and 3 tutorials were carefully reviewed and selected from 457 submissions. They were organized in topical sections named: Part I: deep learning I; entities; evaluation; recommendation; information extraction; deep learning II; retrieval; multimedia; deep learning III; queries; IR – general; question answering, prediction, and bias; and deep learning IV. Part II: reproducibility papers; short papers; demonstration papers; CLEF organizers lab track; doctoral consortium papers; workshops; and tutorials. *Due to the COVID-19 pandemic, this conference was held virtually.
Book Synopsis Text Mining by : Ashok N. Srivastava
Download or read book Text Mining written by Ashok N. Srivastava and published by CRC Press. This book was released on 2009-06-15 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the FieldGiving a broad perspective of the field from numerous vantage points, Text Mining: Classification, Clustering, and Applications focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify te
Book Synopsis International Conference on Innovative Computing and Communications by : Aboul Ella Hassanien
Download or read book International Conference on Innovative Computing and Communications written by Aboul Ella Hassanien and published by Springer Nature. This book was released on 2023-10-25 with total page 932 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes high-quality research papers presented at the Sixth International Conference on Innovative Computing and Communication (ICICC 2023), which is held at the Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, India, on February 17–18, 2023. Introducing the innovative works of scientists, professors, research scholars, students, and industrial experts in the field of computing and communication, the book promotes the transformation of fundamental research into institutional and industrialized research and the conversion of applied exploration into real-time applications.
Book Synopsis Text Classification Aided by Clustering: a Literature Review by : Antonia Kyriakopoulou
Download or read book Text Classification Aided by Clustering: a Literature Review written by Antonia Kyriakopoulou and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We presented several clustering methods for dimensionality reduction to improve text classification. Experiments show that one-way clustering is more effective than feature selection, especially at lower number of features. Also, when dimensionality is reduced by as much as two orders of magnitude the resulting classification accuracy is similar to a fullfeature classifier. In some cases of small training sets and noisy features, feature clustering can actually increase classification accuracy. In the case of IB, various heuristics can be applied in order to obtain finer clusters, greedy agglomerative hard clustering (Slonim & Tishby, 1999), or a sequential K-means like algorithm (Slonim et al., 2002). Co-clustering methods are superior to one-way clustering methods as shown through corresponding experiments (Takamura, 2003). Benefits of using one-way clustering and co-clustering as a feature compression and/or extraction method include: useful semantic feature clusters, higher classification accuracy (via noise reduction), and smaller classification models. The second two reasons are shared with feature selection, and thus clustering can be seen as an alternative or a complement to feature selection, although it does not actually remove any features. Clustering is better at reducing the number of redundant features, whereas feature selection is better at removing detrimental, noisy features. The reduced dimensionality allows the use of more complex algorithms, and reduces computational burden. However, it is necessary to experimentally evaluate the trade-off between soft and hard clustering. While soft clustering increases the classification model size, it is not clear how it affects classification accuracy. Other directions for exploration include feature weighting and combination of feature selection and clustering strategies. There are four cases of semi-supervised classification using clustering considered in the area. In the first case, in the absence of a labelled set, clustering is used to create one by selecting unlabelled data from a pool of available unlabelled data. In the second case, it is used to augment an existing labelled set with new documents from the unlabelled data. In the third case, the dataset is augmented with new features derived from clustering labelled and unlabelled data. In the last case, clustering is used under a co-training framework. The algorithms presented demonstrate effective use of unlabelled data and significant improvements in classification performance especially when the size of the labelled set is small. In most experiments, the unlabelled data come from the same information source as the training and testing sets. Since the feature distribution of the unlabelled data is crucial to the success of the method, an area of future research is the effect of the source and nature of information in the unlabelled dataset and clustering. Lastly, clustering reduces the training time of the SVM i) by modifying the SVM algorithm so that it can be applied to large data sets, and ii) by finding and using for training only the most qualified training examples of a large data set and disqualifying unimportant ones. A clustering algorithm and a classifier cooperate and act interchangeably and complementary. In the first case, many algorithms have been proposed (sequential minimal optimisation, projected conjugate gradient, neural networks amongst others) in order to simplify the training process of SVM, usually by breaking down the problem into smaller sub-problems easier to solve. In the second case, the training set is clustered in order to select the most representative examples to train a classifier instead of using the whole training set. The clustering results are used differently by the various approaches, i.e. the selection of the representative training examples follows different methods. Some of the proposed algorithms manage to decrease the number of training examples without compromising the.
Book Synopsis Automated Taxonomy Discovery and Exploration by : Jiaming Shen
Download or read book Automated Taxonomy Discovery and Exploration written by Jiaming Shen and published by Springer Nature. This book was released on 2022-09-28 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a principled data-driven framework that progressively constructs, enriches, and applies taxonomies without leveraging massive human annotated data. Traditionally, people construct domain-specific taxonomies by extensive manual curations, which is time-consuming and costly. In today’s information era, people are inundated with the vast amounts of text data. Despite their usefulness, people haven’t yet exploited the full power of taxonomies due to the heavy curation needed for creating and maintaining them. To bridge this gap, the authors discuss automated taxonomy discovery and exploration, with an emphasis on label-efficient machine learning methods and their real-world usages. Taxonomy organizes entities and concepts in a hierarchy way. It is ubiquitous in our daily life, ranging from product taxonomies used by online retailers, topic taxonomies deployed by news outlets and social media, as well as scientific taxonomies deployed by digital libraries across various domains. When properly analyzed, these taxonomies can play a vital role for science, engineering, business intelligence, policy design, e-commerce, and more. Intuitive examples are used throughout enabling readers to grasp concepts more easily.
Book Synopsis The Elements of Statistical Learning by : Trevor Hastie
Download or read book The Elements of Statistical Learning written by Trevor Hastie and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
Download or read book Data Mining written by Jiawei Han and published by Morgan Kaufmann. This book was released on 2022-07-02 with total page 786 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets. After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining. Presents a comprehensive new chapter on deep learning, including improving training of deep learning models, convolutional neural networks, recurrent neural networks, and graph neural networks Addresses advanced topics in one dedicated chapter: data mining trends and research frontiers, including mining rich data types (text, spatiotemporal data, and graph/networks), data mining applications (such as sentiment analysis, truth discovery, and information propagattion), data mining methodologie and systems, and data mining and society Provides a comprehensive, practical look at the concepts and techniques needed to get the most out of your data
Book Synopsis Foundations of Intelligent Systems by : Denis Helic
Download or read book Foundations of Intelligent Systems written by Denis Helic and published by Springer Nature. This book was released on 2020-09-17 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 25th International Symposium on Foundations of Intelligent Systems, ISMIS 2020, held in Graz, Austria, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 35 full and 8 short papers presented in this volume were carefully reviewed and selected from 79 submissions. Included is also one invited talk. The papers deal with topics such as natural language processing; deep learning and embeddings; digital signal processing; modelling and reasoning; and machine learning applications.
Book Synopsis Natural Language Processing with Python Quick Start Guide by : Nirant Kasliwal
Download or read book Natural Language Processing with Python Quick Start Guide written by Nirant Kasliwal and published by Packt Publishing Ltd. This book was released on 2018-11-30 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learning Key FeaturesA no-math, code-driven programmer’s guide to text processing and NLPGet state of the art results with modern tooling across linguistics, text vectors and machine learningFundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorchBook Description NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a workflow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges. What you will learnUnderstand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpusWork with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clusteringDeep Learning in NLP using PyTorch with a code-driven introduction to PyTorchUsing an NLP project management Framework for estimating timelines and organizing your project into stagesHack and build a simple chatbot application in 30 minutesDeploy an NLP or machine learning application using Flask as RESTFUL APIsWho this book is for Programmers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.
Book Synopsis Modern Technologies for Big Data Classification and Clustering by : Seetha, Hari
Download or read book Modern Technologies for Big Data Classification and Clustering written by Seetha, Hari and published by IGI Global. This book was released on 2017-07-12 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage. Modern Technologies for Big Data Classification and Clustering is an essential reference source for the latest scholarly research on handling large data sets with conventional data mining and provide information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is ideally designed for professionals, researchers, and students seeking current research on various concepts of big data analytics.
