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Time and Space Efficient web document clustering Using Rayleigh Distribution

WIRELESS PERSONAL COMMUNICATIONS 2018年第4期102卷 3255-3268页

作者： Srikanth, D. Sakthivel, S. Vidyaa Vikas Coll Engn & Technol Dept Comp Sci & Engn Namakkal Tamil Nadu India Sona Coll Technol Dept Comp Sci & Engn Salem India

web document clustering identifies the relevant and useful information like comparing shopping service provider from ***, information retrieval from web search engines and so on. Choosing the best representation and enhancing knowledge discovery for a given task in very large textual data stores is the most critical step in web document clustering. In this work, considering the problem of discovering the most predominant word with similar semantic model and measuring relative strength of predominant word of web document. This paper presents an efficient technique called Rayleigh clustering with Self Organizing Map (RC-SOM) for web document domain using generation of self organizing patterns, clustering of predominant word and Rayleigh distribution. Self organizing patterns are generated to identify the most predominant word from web document. Then clustering of predominant word with similar semantic are organized for all the web documents. Finally, the efficiency of web document clustering is improved by applying Rayleigh distribution that lists out the relative strength of predominant word for each web document. The experimental is presented for RC-SOM technique on Anonymous Microsoft web Data dataset and performs evolution factor such as cluster accuracy, execution time and computation space for cluster.

关键词： Rayleigh clustering semantic model web document clustering Rayleigh distribution Partially Supervised Alignment for Online Reviews (PSA-OR) web document clustering Self-Organizing Map (SOM)

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WeDoCWT: A New Method for web document clustering Using Discrete Wavelet Transforms

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2017年第1期16卷 19-19页

作者： Al-Mofareji, Hanan Kamel, Mahmoud Dahab, Mohamed Y. King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Sci Jeddah 23218 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah 23218 Saudi Arabia

Organizing web information is an important aspect of finding information in the easiest and most efficient way. We present a new method for web document clustering called WeDoCWT, which exploits the discrete wavelet transform and term signal, to improve the document representation. We studied different methods for document segmentation to construct the term signals. We used two datasets, UW-CAN and webKB, to evaluate the proposed method. The experimental results indicated that dividing the documents into fixed segments is preferable to dividing them into logical segments based on HTML features because the web pages do not have the same structure. Mean TF-IDF reduction technique gives the best results in most cases. WeDoCWT gives F-measure better than most of the previous approaches described in the literature. We used Munkres assignment algorithm to assign each produced cluster to the original class in order to evaluate the clustering results.

关键词： Term signal document segmentation web document representation web document clustering wavelet transform

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Improving web document clustering through Employing User-Related Tag Expansion Techniques

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Journal of Computer Science & Technology 2012年第3期27卷 554-566页

作者：李鹏王斌晋薇 Institute of Computing Technology Chinese Academy of Sciences Department of Computer Science North Dakota State University1340 Administration Ave.FargoND 58102U.S.A.

As high quality descriptors of web page semantics, social annotations or tags have been used for web document clustering and achieved promising results. However, most web pages have few tags （less than 10）. This sparsity seriously limits the usage of tags for clustering. In this work, we propose a user-related tag expansion method to overcome this problem, which incorporates additional useful tags into the original tag document by utilizing user tagging data as background knowledge. Unfortunately, simply adding tags may cause topic drift, i.e., the dominant topic（s） of the original document may be changed. To tackle this problem, we have designed a novel generative model called Folk-LDA, which jointly models original and expanded tags as independent observations. Experimental results show that 1） our user-related tag expansion method can be effectively applied to over 90% tagged web documents; 2） Folk-LDA can alleviate topic drift in expansion, especially for those topic-specific documents; 3） the proposed tag-based clustering methods significantly outperform the word-based methods., which indicates that tags could be a better resource for the clustering task.

关键词： web document clustering social bookmarking topic model tag expansion

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Fitness Function Obtained from a Genetic Programming Approach for web document clustering Using Evolutionary Algorithms

Fitness Function Obtained from a Genetic Programming Approac...

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13th Biennial Ibero-American Conference on Artificial Intelligence (AI)

作者： Cobos, Carlos Munoz, Leydy Mendoza, Martha Leon, Elizabeth Herrera-Viedma, Enrique Univ Cauca Dept Comp Sci Bogota Colombia Univ Nacl Colombia Fac Engn Syst & Ind Engn Dept Bogota Colombia Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain

ISBN: (纸本)9783642346538;9783642346545

web document clustering (WDC) is an alternative means of searching the web and has become a rewarding research area. Algorithms for WDC still present some problems, in particular: inconsistencies in the content and description of clusters. The use of evolutionary algorithms is one approach for improving results. It uses standard index to evaluate the quality (as a fitness function) of different solutions of clustering. Indexes such as Bayesian Information Criteria (BIC), Davies-Bouldin, and others show good performance, but with much room for improvement. In this paper, a modified BIC fitness function for WDC based on evolutionary algorithms is presented. This function was discovered using a genetic program (from a reverse engineering view). Experiments on datasets based on DMOZ show promising results.

关键词： genetic programming web document clustering clustering of web results Bayesian information criteria

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A Hyper-Heuristic Approach To Design And Tuning Heuristic Methods For web document clustering

A Hyper-Heuristic Approach To Design And Tuning Heuristic Me...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Cobos, Carlos Mendoza, Martha Leon, Elizabeth Univ Cauca Dept Comp Sci Popayan Colombia Univ Nacl Colombia Syst & Ind Dept Bogota Colombia

ISBN: (纸本)9781424478354

This paper introduces a new description-centric algorithm for web document clustering called HHWDC. The HHWDC algorithm has been designed from a hyper-heuristic approach and allows defining the best algorithm for web document clustering. HHWDC uses as heuristic selection methodology two options, namely: random selection and roulette wheel selection based on performance of low-level heuristics (harmony search, an improved harmony search, a novel global harmony search, global-best harmony search, restrictive mating, roulette wheel selection, and particle swarm optimization). HHWDC uses the k-means algorithm for local solution improvement strategy, and based on the Bayesian Information Criteria is able to automatically define the number of clusters. HHWDC uses two acceptance/replace strategies, namely: Replace the worst and Restricted Competition Replacement. HHWDC was tested with data sets based on Reuters-21578 and DMOZ, obtaining promising results (better precision results than a Singular Value Decomposition algorithm).

关键词： web document clustering hyper-heuristic genetic algorithm memetic algorithm harmony search particle swarm

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Algorithm for clustering of web Search Results from a Hyper-heuristic Approach 15th

Algorithm for Clustering of Web Search Results from a Hyper-...

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15th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Cobos, Carlos Duque, Andrea Bolanos, Jamith Mendoza, Martha Leon, Elizabeth Univ Cauca Popayan Colombia Univ Nacl Colombia Bogota Colombia

ISBN: (纸本)9783319624280;9783319624273

The clustering of web search results - or web document clustering (WDC) -has become a very interesting research area among academic and scientific communities involved in information retrieval. Systems for the clustering of web search results, also called web clustering Engines, seek to increase the coverage of documents presented for the user to review, while reducing the time spent reviewing them. Several algorithms for clustering of web results already exist, but results show there is room for more to be done. This paper introduces a hyper-heuristic framework called WDC-HH, which allows the defining of the best algorithm for WDC. The hyper-heuristic framework uses four high-level-heuristics (performance-based rank selection, tabu selection, random selection and performance-based roulette wheel selection) for selecting low-level heuristics (used to solve the specific problem of WDC). As a low level heuristics the framework considers: harmony search, improved harmony search, novel global harmony search, global-best harmony search, eighteen genetic algorithm variations, particle swarm optimization, artificial bee colony, and differential evolution. The framework uses the k-means algorithm as a local solution improvement strategy and based on the Balanced Bayesian Information Criterion it is able to automatically define the appropriate number of clusters. The framework also uses four acceptance/replacement strategies (replacement heuristics): Replace the worst, Restricted Competition Replacement, Stochastic Replacement and Rank Replacement. WDC-HH was tested with four data sets using a total of 447 queries with their ideal solutions. As a main result of the framework assessment, a new algorithm based on global-best harmony search and rank replacement strategy obtained the best results in WDC problem. This new algorithm was called WDC-HH-BHRK and was also compared against other established WDC algorithms, among them: Suffix Tree clustering (STC) and Lingo. Results sho

关键词： Hyper-heuristics clustering of web results web document clustering Balanced Bayesian Information Criterion K-means

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A Novel Approach to Aggregation of web documents by Semantic Suffix Tree and Self Organizing Feature Map Methods 3

A Novel Approach to Aggregation of Web Documents by Semantic...

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3rd IEEE International Conference on Science Technology Engineering & Management (ICONSTEM)

作者： Thilagavathy, R. Sabitha, R. Sathyabama Univ CSE Dept Madras 119 Tamil Nadu India Jeppiaar Engn Coll IT Dept Madras 119 Tamil Nadu India

ISBN: (纸本)9781509048557

The amount of information available on the internet became enormously high because of the rapid development in the electronic medium. Now days, managing and retrieving useful information from the web becomes the very tedious job. web document clustering is a methodology which tries to group the available web documents in a meaningful way. There are lot of issues are identified in this area like, heterogeneous sources of information, no universal model, dynamicity of information, the huge volume of data, etc. most of the existing document clustering models follows the "bag-of-words" document representation. The proposed model presents an incremental analysis of web documents based on semantic suffix tree which analyses web documents by means of concepts to improve the cluster quality. The concepts are evaluated at three levels, a sentence level analysis, a document level analysis and domain level analysis. The proposed model also uses self organizing feature map (SOFM) to group similar documents together in a cluster and organize similar clusters close together unlike most other clustering methods. A set of experiments using the proposed model on different data sets have been conducted. The experiment result shows a significant improvement in the cluster quality.

关键词： text mining web document clustering term frequency conceptual term frequency concept-based similarity measure suffix tree clustering SOFM

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A Concept Driven document clustering Using WordNet

A Concept Driven Document Clustering Using WordNet

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International Conference on Nascent Technologies in Engineering (ICNTE)

作者： Kolhe, Sujata R. Sawarkar, S. D. Datta Meghe Coll Engn Dept Comp Engn Navi Mumbai Airoli India

ISBN: (纸本)9781509027941

Recently Information Technology is used extensively for wide range of application for example solutions enabled through e-commerce to different web based information system. This usage has lead to development of large textual data base. Mostly this information data is stored in unstructured text. This large data developed has lead to the need of its systematic clustering for easy data retrieval organization and summarization, typically called as data mining. Many techniques such as document clustering, web based result clustering and information visualization are developed by the researchers based on various algorithms such as k-means, k-medoid, Bisecting k-means, Suffix Tree clustering (STC), Hierarchical clustering, Lingo and nature inspired optimization algorithm like clustering using Ant colony optimization, Fireflies and so on. Although these techniques are widely used, these are suffered from inconsistencies in cluster content and inconsistencies in cluster description. Therefore this paper presents a novel approach termed as 'Semantic Lingo' based on concept of the text data. This concept driven approach is executed on Wordnet. The developed approach identifies the predominant notion and automatically generate clusters based on these notion. The standard available dataset ODP, AMBIENT are used in the paper for validation of the proposed concept. The data clusters obtained from the proposed concept has shown high precision level and recall.

关键词： web document clustering Concepts Semantic Word Net cluster labels

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web document clustering based on Global-Best Harmony Search, K-means, Frequent Term Sets and Bayesian Information Criterion

Web document clustering based on Global-Best Harmony Search,...

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2010 IEEE World Congress on Computational Intelligence

作者： Cobos, Carlos Andrade, Jennifer Constain, William Mendoza, Martha Leon, Elizabeth Univ Cauca Popayan Colombia Univ Nacl Colombia Bogota Colombia

ISBN: (纸本)9781424481262

This paper introduces a new description-centric algorithm for web document clustering based on the hybridization of the Global-Best Harmony Search with the K-means algorithm, Frequent Term Sets and Bayesian Information Criterion. The new algorithm defines the number of clusters automatically. The Global-Best Harmony Search provides a global strategy for a search in the solution space, based on the Harmony Search and the concept of swarm intelligence. The K-means algorithm is used to find the optimum value in a local search space. Bayesian Information Criterion is used as a fitness function, while FP-Growth is used to reduce the high dimensionality in the vocabulary. This resulting algorithm, called IGBHSK, was tested with data sets based on Reuters-21578 and DMOZ, obtaining promising results (better precision results than a Singular Value Decomposition algorithm). Also, it was also then evaluated by a group of users.

关键词： Bayes methods Bayesian information criterion Internet web document clustering description-centric algorithm document handling frequent term sets global-best harmony search k-means clustering local search space particle swarm optimisation pattern clustering search problems swarm intelligence vocabulary

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web document clustering based on a New Niching Memetic Algorithm, Term-document Matrix and Bayesian Information Criterion

Web Document Clustering based on a New Niching Memetic Algor...

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2010 IEEE World Congress on Computational Intelligence

作者： Cobos, Carlos Montealegre, Claudia Mejia, Maria-Fernanda Mendoza, Martha Leon, Elizabeth Univ Cauca Popayan Colombia Univ Nacl Colombia Bogota Colombia

ISBN: (纸本)9781424481262

This paper introduces a new description-centric algorithm for web document clustering based on Memetic Algorithms with Niching Methods, Term-document Matrix and Bayesian Information Criterion. The algorithm defines the number of clusters automatically. The Memetic Algorithm provides a combined global and local strategy for a search in the solution space and the Niching methods to promote diversity in the population and prevent the population from converging too quickly (based on restricted competition replacement and restrictive mating). The Memetic Algorithm uses the K-means algorithm to find the optimum value in a local search space. Bayesian Information Criterion is used as a fitness function, while FP-Growth is used to reduce the high dimensionality in the vocabulary. This resulting algorithm, called WDC-NMA, was tested with data sets based on Reuters-21578 and DMOZ, obtaining promising results (better precision results than a Singular Value Decomposition algorithm). Also, it was also then initially evaluated by a group of users.

关键词： Bayes methods Bayesian information criterion DMOZ FP-growth Internet Reuters-21578 WDC-NMA web document clustering description-centric algorithm document handling fitness function genetic algorithms information retrieval k-means algorithm matrix algebra niching memetic algorithm niching methods search space singular value decomposition singular value decomposition algorithm term-document matrix vocabulary vocabulary

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