A Feature Reduction Technique for Improved Web Page Clustering

2006 ◽  
Author(s):  
Ehab Mohamed ◽  
Samhaa El-beltagy ◽  
Salwa El-Gamal
Keyword(s):  
Reduction Technique ◽  
Feature Reduction ◽  
Web Page ◽  
Web Page Clustering

Improved Web page clustering algorithm based on partial tag tree matching

2010 ◽  
Vol 30 (3) ◽  
pp. 818-820
Author(s):  
Rui LI ◽  
Jun-yu ZENG ◽  
Si-wang ZHOU
Keyword(s):  
Clustering Algorithm ◽  
Web Page ◽  
Web Page Clustering

scholarly journals A Web Page Clustering Method Based on Formal Concept Analysis

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 228 ◽  
Author(s):  
Zuping Zhang ◽  
Jing Zhao ◽  
Xiping Yan
Keyword(s):  
Formal Concept Analysis ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Formal Context ◽  
Formal Concept ◽  
Web Pages ◽  
Web Page ◽  
Data Links ◽  
Web Page Clustering ◽  
The Web

Web page clustering is an important technology for sorting network resources. By extraction and clustering based on the similarity of the Web page, a large amount of information on a Web page can be organized effectively. In this paper, after describing the extraction of Web feature words, calculation methods for the weighting of feature words are studied deeply. Taking Web pages as objects and Web feature words as attributes, a formal context is constructed for using formal concept analysis. An algorithm for constructing a concept lattice based on cross data links was proposed and was successfully applied. This method can be used to cluster the Web pages using the concept lattice hierarchy. Experimental results indicate that the proposed algorithm is better than previous competitors with regard to time consumption and the clustering effect.

scholarly journals A hybrid sentiment analysis approach using black widow optimization based feature selection

2021 ◽  
Author(s):  
Anand Joseph Daniel ◽  
◽  
M Janaki Meena ◽  
Keyword(s):  
Feature Selection ◽  
Sentiment Analysis ◽  
Performance Metrics ◽  
Computation Time ◽  
Online Reviews ◽  
Reduction Technique ◽  
Feature Reduction ◽  
Analysis Approach ◽  
Black Widow ◽  
Feature Selection Technique

With the massive development of Internet technologies and e-commerce technology, people rely on the product reviews provided by users through web. Sentiment analysis of online reviews has become a mainstream way for businesses on e-commerce platforms to satisfy the customers. This paper proposes a novel hybrid framework with Black Widow Optimization (BWO) based feature reduction technique which combines the merits of both machine learning and lexicon-based approaches to attain better scalability and accuracy. The scalability problem arises due to noisy, irrelevant and unique features present in the extracted features from proposed approach, which can be eliminated by adopting an effective feature reduction technique. In our proposed BWO approach, without changing the accuracy (90%), the feature-set size is reduced up to 43%. The proposed feature selection technique outperforms other commonly used Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) based feature selection techniques with reduced computation time of 21 sec. Moreover, our sentiment analysis approach is analyzed using performance metrics such as precision, recall, F-measure, and computation time. Many organizations can use these online reviews to make well-informed decisions towards the users’ interests and preferences to enhance customer satisfaction, product quality and to find the aspects to improve the products, thereby to generate more profits.

scholarly journals Techniques for Improving Web Search by  Understanding Queries

2021 ◽  
Author(s):  
◽  
Daniel Wayne Crabtree
Keyword(s):  
Search Engines ◽  
Best Practice ◽  
Web Search ◽  
Special Focus ◽  
Clustering Methods ◽  
Web Page ◽  
Clustering Method ◽  
Evaluation Measures ◽  
Search Results ◽  
Web Page Clustering

<p>This thesis investigates the refinement of web search results with a special focus on the use of clustering and the role of queries. It presents a collection of new methods for evaluating clustering methods, performing clustering effectively, and for performing query refinement. The thesis identifies different types of query, the situations where refinement is necessary, and the factors affecting search difficulty. It then analyses hard searches and argues that many of them fail because users and search engines have different query models. The thesis identifies best practice for evaluating web search results and search refinement methods. It finds that none of the commonly used evaluation measures for clustering meet all of the properties of good evaluation measures. It then presents new quality and coverage measures that satisfy all the desired properties and that rank clusterings correctly in all web page clustering situations. The thesis argues that current web page clustering methods work well when different interpretations of the query have distinct vocabulary, but still have several limitations and often produce incomprehensible clusters. It then presents a new clustering method that uses the query to guide the construction of semantically meaningful clusters. The new clustering method significantly improves performance. Finally, the thesis explores how searches and queries are composed of different aspects and shows how to use aspects to reduce the distance between the query models of search engines and users. It then presents fully automatic methods that identify query aspects, identify underrepresented aspects, and predict query difficulty. Used in combination, these methods have many applications — the thesis describes methods for two of them. The first method improves the search results for hard queries with underrepresented aspects by automatically expanding the query using semantically orthogonal keywords related to the underrepresented aspects. The second method helps users refine hard ambiguous queries by identifying the different query interpretations using a clustering of a diverse set of refinements. Both methods significantly outperform existing methods.</p>

scholarly journals Techniques for Improving Web Search by  Understanding Queries

2021 ◽  
Author(s):  
◽  
Daniel Wayne Crabtree
Keyword(s):  
Search Engines ◽  
Best Practice ◽  
Web Search ◽  
Special Focus ◽  
Clustering Methods ◽  
Web Page ◽  
Clustering Method ◽  
Evaluation Measures ◽  
Search Results ◽  
Web Page Clustering

<p>This thesis investigates the refinement of web search results with a special focus on the use of clustering and the role of queries. It presents a collection of new methods for evaluating clustering methods, performing clustering effectively, and for performing query refinement. The thesis identifies different types of query, the situations where refinement is necessary, and the factors affecting search difficulty. It then analyses hard searches and argues that many of them fail because users and search engines have different query models. The thesis identifies best practice for evaluating web search results and search refinement methods. It finds that none of the commonly used evaluation measures for clustering meet all of the properties of good evaluation measures. It then presents new quality and coverage measures that satisfy all the desired properties and that rank clusterings correctly in all web page clustering situations. The thesis argues that current web page clustering methods work well when different interpretations of the query have distinct vocabulary, but still have several limitations and often produce incomprehensible clusters. It then presents a new clustering method that uses the query to guide the construction of semantically meaningful clusters. The new clustering method significantly improves performance. Finally, the thesis explores how searches and queries are composed of different aspects and shows how to use aspects to reduce the distance between the query models of search engines and users. It then presents fully automatic methods that identify query aspects, identify underrepresented aspects, and predict query difficulty. Used in combination, these methods have many applications — the thesis describes methods for two of them. The first method improves the search results for hard queries with underrepresented aspects by automatically expanding the query using semantically orthogonal keywords related to the underrepresented aspects. The second method helps users refine hard ambiguous queries by identifying the different query interpretations using a clustering of a diverse set of refinements. Both methods significantly outperform existing methods.</p>

Support Vector Machines and Affective Science

2020 ◽  
Vol 12 (4) ◽  
pp. 297-308
Author(s):  
Chris H. Miller ◽  
Matthew D. Sacchet ◽  
Ian H. Gotlib
Keyword(s):  
Support Vector Machines ◽  
Reduction Technique ◽  
Feature Reduction ◽  
Data Driven ◽  
Future Research ◽  
Support Vector ◽  
Predictive Algorithm ◽  
Affective Science ◽  
Vector Machines ◽  
Potential Applications

Support vector machines (SVMs) are being used increasingly in affective science as a data-driven classification method and feature reduction technique. Whereas traditional statistical methods typically compare group averages on selected variables, SVMs use a predictive algorithm to learn multivariate patterns that optimally discriminate between groups. In this review, we provide a framework for understanding the methods of SVM-based analyses and summarize the findings of seminal studies that use SVMs for classification or data reduction in the behavioral and neural study of emotion and affective disorders. We conclude by discussing promising directions and potential applications of SVMs in future research in affective science.

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