High Performance Text Document Clustering

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High Performance Text Document Clustering

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Author : Yanjun Li
language : en
Publisher:
Release Date : 2007

High Performance Text Document Clustering written by Yanjun Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Algorithms categories.

Data mining, also known as knowledge discovery in database (KDD), is the process to discover interesting unknown knowledge from a large amount of data. Text mining is to apply data mining techniques to extract interesting and nontrivial information and knowledge from unstructured text. Text clustering is one of important techniques of text mining, which is the unsupervised classification of similar documents into different groups. This research focuses on improving the performance of text clustering. We investigated the text clustering algorithms in four aspects: document representation, documents closeness measurement, high dimension reduction and parallelization. We propose a group of high performance text clustering algorithms, which target the unique characteristics of unstructured text database. First, two new text clustering algorithms are proposed. Unlike the vector space model, which treats document as a bag of words, we use a document representation which keeps the sequential relationship between words in the documents. In these two algorithms, the dimension of the database is reduced by considering the frequent word (meaning) sequences, and the closeness of two documents is measured based on the sharing of frequent word (meaning) sequences. Second, a text clustering algorithm with feature selection is proposed. This algorithm gradually reduces the high dimension of database by performing feature selection during the clustering. The new feature selection method applied is based on the well-known chi-square statistic and a new statistical data which can measure the positive and negative term-category dependence. Third, a group of new text clustering algorithms is developed based on the k-means algorithm. Instead of using the cosine function, a new function involving global information is proposed to measure the closeness between two documents. This new function utilizes the neighbor matrix introduced in [Guha:2000]. A new method for selecting initial centroids and a new heuristic function for selecting a cluster to split are adopted in the proposed algorithms. Last, a new parallel algorithm for bisecting k-means is proposed for the message-passing multiprocessor systems. This new algorithm, named PBKP, fully utilizes the data-parallelism of the bisecting k-means algorithm, and adopts a prediction step to balance the workloads of multiple processors to achieve a high speedup. Comprehensive performance studies were conducted on all the proposed algorithms. In order to evaluate the performance of these algorithms, we compared them with existing text clustering algorithms, such as k-means, bisecting k-means [Steinbach:2000] and FIHC [Fung:2003]. The experimental results show that our clustering algorithms are scalable and have much better clustering accuracy than existing algorithms. For the parallel PBKP algorithm, we tested it on a 9-node Linux cluster system and analyzed its performance. The experimental results suggest that the speedup of PBKP is linear with the number of processors and data points. Moreover, PBKP scales up better than the parallel k-means with respect to the desired number of clusters.

Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering

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Author : Laith Mohammad Qasim Abualigah
language : en
Publisher: Springer
Release Date : 2018-12-18

Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering written by Laith Mohammad Qasim Abualigah and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-18 with Technology & Engineering categories.

This book puts forward a new method for solving the text document (TD) clustering problem, which is established in two main stages: (i) A new feature selection method based on a particle swarm optimization algorithm with a novel weighting scheme is proposed, as well as a detailed dimension reduction technique, in order to obtain a new subset of more informative features with low-dimensional space. This new subset is subsequently used to improve the performance of the text clustering (TC) algorithm and reduce its computation time. The k-mean clustering algorithm is used to evaluate the effectiveness of the obtained subsets. (ii) Four krill herd algorithms (KHAs), namely, the (a) basic KHA, (b) modified KHA, (c) hybrid KHA, and (d) multi-objective hybrid KHA, are proposed to solve the TC problem; each algorithm represents an incremental improvement on its predecessor. For the evaluation process, seven benchmark text datasets are used with different characterizations and complexities. Text document (TD) clustering is a new trend in text mining in which the TDs are separated into several coherent clusters, where all documents in the same cluster are similar. The findings presented here confirm that the proposed methods and algorithms delivered the best results in comparison with other, similar methods to be found in the literature.

Mining Text Data

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Author : Charu C. Aggarwal
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-02-03

Mining Text Data written by Charu C. Aggarwal 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 2012-02-03 with Computers categories.

Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.

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.

Successful Culturing Of Glover S Cancer Organism And Development Of Metastasizing Tumors In Animals Produced By Cultures From Human Malignancy

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Author :
language : en
Publisher:
Release Date : 1953

Successful Culturing Of Glover S Cancer Organism And Development Of Metastasizing Tumors In Animals Produced By Cultures From Human Malignancy written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1953 with categories.

An Improved Clustering Method For Text Documents Using Neutrosophic Logic

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Author : Nadeem Akhtar
language : en
Publisher: Infinite Study
Release Date :

An Improved Clustering Method For Text Documents Using Neutrosophic Logic written by Nadeem Akhtar and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

As a technique of Information Retrieval, we can consider clustering as an unsupervised learning problem in which we provide a structure to unlabeled and unknown data.

Practical Data Mining Techniques And Applications

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Author : Ketan Shah
language : en
Publisher: CRC Press
Release Date : 2023-06-19

Practical Data Mining Techniques And Applications written by Ketan Shah and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-19 with Computers categories.

Unique selling point: Applied data mining techniques in multiple domains and real-world settings Core audience: Researchers, graduate and post graduate students, and academics Place in the market: Applied technology reference book

Incorporating Semantic And Syntactic Information Into Document Representation For Document Clustering

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Author :
language : en
Publisher:
Release Date : 2005

Incorporating Semantic And Syntactic Information Into Document Representation For Document Clustering written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.

Document clustering is a widely used strategy for information retrieval and text data mining. In traditional document clustering systems, documents are represented as a bag of independent words. In this project, we propose to enrich the representation of a document by incorporating semantic information and syntactic information. Semantic analysis and syntactic analysis are performed on the raw text to identify this information. A detailed survey of current research in natural language processing, syntactic analysis, and semantic analysis is provided. Our experimental results demonstrate that incorporating semantic information and syntactic information can improve the performance of our document clustering system for most of our data sets. A statistically significant improvement can be achieved when we combine both syntactic and semantic information. Our experimental results using compound words show that using only compound words does not improve the clustering performance for our data sets. When the compound words are combined with original single words, the combined feature set gets slightly better performance for most data sets. But this improvement is not statistically significant. In order to select the best clustering algorithm for our document clustering system, a comparison of several widely used clustering algorithms is performed. Although the bisecting K-means method has advantages when working with large datasets, a traditional hierarchical clustering algorithm still achieves the best performance for our small datasets.

Applications Of Advanced Optimization Techniques In Industrial Engineering

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Author : Abhinav Goel
language : en
Publisher: CRC Press
Release Date : 2022-03-10

Applications Of Advanced Optimization Techniques In Industrial Engineering written by Abhinav Goel and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-10 with Mathematics categories.

This book provides different approaches used to analyze, draw attention, and provide an understanding of the advancements in the optimization field across the globe. It brings all of the latest methodologies, tools, and techniques related to optimization and industrial engineering into a single volume to build insights towards the latest advancements in various domains. Applications of Advanced Optimization Techniques in Industrial Engineering includes the basic concept of optimization, techniques, and applications related to industrial engineering. Concepts are introduced in a sequential way along with explanations, illustrations, and solved examples. The book goes on to explore applications of operations research and covers empirical properties of a variety of engineering disciplines. It presents network scheduling, production planning, industrial and manufacturing system issues, and their implications in the real world. The book caters to academicians, researchers, professionals in inventory analytics, business analytics, investment managers, finance firms, storage-related managers, and engineers working in engineering industries and data management fields.

Advances In Natural Language Processing Intelligent Informatics And Smart Technology

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Author : Thanaruk Theeramunkong
language : en
Publisher: Springer
Release Date : 2018-03-15

Advances In Natural Language Processing Intelligent Informatics And Smart Technology written by Thanaruk Theeramunkong and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-15 with Technology & Engineering categories.

This book constitutes the thoroughly refereed proceedings of the Eleventh International Symposium on Natural Language Processing (SNLP-2016), held in Phranakhon Si Ayutthaya, Thailand on February 10–12, 2016. The SNLP promotes research in natural language processing and related fields, and provides a unique opportunity for researchers, professionals and practitioners to discuss various current and advanced issues of interest in NLP. The 2016 symposium was expanded to include the First Workshop in Intelligent Informatics and Smart Technology. Of the 66 high-quality papers accepted, this book presents twelve from the Symposium on Natural Language Processing track and ten from the Workshop in Intelligent Informatics and Smart Technology track (SSAI: Special Session on Artificial Intelligence).