Text Classification Aided By Clustering A Literature Review
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Text Classification Aided By Clustering A Literature Review
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Author : Antonia Kyriakopoulou
language : en
Publisher:
Release Date : 2008
Text Classification Aided By Clustering A Literature Review written by Antonia Kyriakopoulou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
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.
Text Mining
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Author : Ashok N. Srivastava
language : en
Publisher: Chapman and Hall/CRC
Release Date : 2009-06-15
Text Mining written by Ashok N. Srivastava and has been published by Chapman and Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-15 with Computers categories.
The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the Field Giving 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 text documents and applies these methods in a variety of areas, including adaptive information filtering, information distillation, and text search. The book begins with chapters on the classification of documents into predefined categories. It presents state-of-the-art algorithms and their use in practice. The next chapters describe novel methods for clustering documents into groups that are not predefined. These methods seek to automatically determine topical structures that may exist in a document corpus. The book concludes by discussing various text mining applications that have significant implications for future research and industrial use. There is no doubt that text mining will continue to play a critical role in the development of future information systems and advances in research will be instrumental to their success. This book captures the technical depth and immense practical potential of text mining, guiding readers to a sound appreciation of this burgeoning field.
Survey Of Text Mining
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Author : Michael W. Berry
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14
Survey Of Text Mining written by Michael W. Berry and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Computers categories.
Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.
Advanced Informatics For Computing Research
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Author : Ashish Kumar Luhach
language : en
Publisher: Springer Nature
Release Date : 2019-09-16
Advanced Informatics For Computing Research written by Ashish Kumar Luhach and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-16 with Computers categories.
This two-volume set (CCIS 1075 and CCIS 1076) constitutes the refereed proceedings of the Third International Conference on Advanced Informatics for Computing Research, ICAICR 2019, held in Shimla, India, in June 2019. The 78 revised full papers presented were carefully reviewed and selected from 382 submissions. The papers are organized in topical sections on computing methodologies; hardware; information systems; networks; software and its engineering.
Counterterrorism And Open Source Intelligence
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Author : Uffe Wiil
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-06-27
Counterterrorism And Open Source Intelligence written by Uffe Wiil and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-27 with Computers categories.
Since the 9/11 terrorist attacks in the United States, serious concerns were raised on domestic and international security issues. Consequently, there has been considerable interest recently in technological strategies and resources to counter acts of terrorism. In this context, this book provides a state-of-the-art survey of the most recent advances in the field of counterterrorism and open source intelligence, demonstrating how various existing as well as novel tools and techniques can be applied in combating covert terrorist networks. A particular focus will be on future challenges of open source intelligence and perspectives on how to effectively operate in order to prevent terrorist activities.
Computational Intelligence And Intelligent Systems
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Author : Zhenhua Li
language : en
Publisher: Springer
Release Date : 2009-11-03
Computational Intelligence And Intelligent Systems written by Zhenhua Li and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-11-03 with Computers categories.
Volumes CCIS 51 and LNCS 5812 constitute the proceedings of the Fourth Interational Symposium on Intelligence Computation and Applications, ISICA 2009, held in Huangshi, China, during October 23-25. ISICA 2009 attracted over 300 submissions. Through rigorous reviews, 58 papers were included in LNCS 5821, and 54 papers were collected in CCIS 51. ISICA conferences are one of the first series of international conferences on computational intelligence that combine elements of learning, adaptation, evolution and fuzzy logic to create programs as alternative solutions to artificial intelligence.
Survey Of Text Mining Ii
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Author : Michael W. Berry
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-12-10
Survey Of Text Mining Ii written by Michael W. Berry and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-12-10 with Computers categories.
This Second Edition brings readers thoroughly up to date with the emerging field of text mining, the application of techniques of machine learning in conjunction with natural language processing, information extraction, and algebraic/mathematical approaches to computational information retrieval. The book explores a broad range of issues, ranging from the development of new learning approaches to the parallelization of existing algorithms. Authors highlight open research questions in document categorization, clustering, and trend detection. In addition, the book describes new application problems in areas such as email surveillance and anomaly detection.
Intelligent Text Categorization And Clustering
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Author : Felipe M. G. França
language : en
Publisher: Springer
Release Date : 2008-09-09
Intelligent Text Categorization And Clustering written by Felipe M. G. França and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-09 with Technology & Engineering categories.
Automatic Text Categorization and Clustering are becoming more and more important as the amount of text in electronic format grows and the access to it becomes more necessary and widespread. Well known applications are spam filtering and web search, but a large number of everyday uses exist (intelligent web search, data mining, law enforcement, etc.) Currently, researchers are employing many intelligent techniques for text categorization and clustering, ranging from support vector machines and neural networks to Bayesian inference and algebraic methods, such as Latent Semantic Indexing. This volume offers a wide spectrum of research work developed for intelligent text categorization and clustering. In the following, we give a brief introduction of the chapters that are included in this book.
Classification Clustering And Data Mining Applications
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Author : International Federation of Classification Societies. Conference
language : en
Publisher: Springer Science & Business Media
Release Date : 2004-06-09
Classification Clustering And Data Mining Applications written by International Federation of Classification Societies. Conference and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-06-09 with Computers categories.
Modern data analysis stands at the interface of statistics, computer science, and discrete mathematics. This volume describes new methods in this area, with special emphasis on classification and cluster analysis. Those methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.
Survey Of Text Mining Ii
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Author :
language : en
Publisher:
Release Date : 2008
Survey Of Text Mining Ii written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.