scholarly journals An Efficient Mining Approach for Handling Web Access Sequences

2021 ◽  
Vol 3 (1) ◽  
pp. 15-25
Author(s):  
Nandhini R ◽  
Evangelin Sonia S.V
Keyword(s):  
Web Mining ◽  
Research Work ◽  
Web Page ◽  
Web Data ◽  
Web Log ◽  
Modern Technique ◽  
Modified Genetic Algorithm ◽  
Tree Construction ◽  
The World ◽  
Web Access

The World Wide Web (WWW) becomes an important source for collecting, storing, and sharing the information. Based on the users query the traditional web page search approximately retrieves the related link and some of the search engines are Alta, Vista, Google, etc. The process of web mining defines to determine the unknown and useful information from web data. Web mining contains the two approaches such as data-based approach and process-based approach. Now a day the data-based approach is the widely used approach. It is used to extract the knowledge from web data in the form of hyper link, and web log data. In this study, the modern technique is presented for mining web access utility-based tree construction under Modified Genetic Algorithm (MGA). MGA tree are newly created to deploy the tree construction. In the web access sequences tree construction for the most part relies upon internal and external utility values. The performance of the proposed technique provides an efficient Web access sequences for both static and incremental data. Furthermore, this research work is helpful for both forward references and backward references of web access sequences.

Extracting Knowledge from Web Data

2014 ◽  
Vol 7 (4) ◽  
pp. 27-41
Author(s):  
Hanane Ezzikouri ◽  
Mohamed Fakir ◽  
Cherki Daoui ◽  
Mohamed Erritali
Keyword(s):  
Web Mining ◽  
User Behavior ◽  
Data Extraction ◽  
Research Area ◽  
Web Usage Mining ◽  
Web Data ◽  
Main Research ◽  
Web Log ◽  
Web Usage ◽  
Log File

The user behavior on a website triggers a sequence of queries that have a result which is the display of certain pages. The Information about these queries (including the names of the resources requested and responses from the Web server) are stored in a text file called a log file. Analysis of server log file can provide significant and useful information. Web Mining is the extraction of interesting and potentially useful patterns and implicit information from artifacts or activity related to the World Wide Web. Web usage mining is a main research area in Web mining focused on learning about Web users and their interactions with Web sites. The motive of mining is to find users' access models automatically and quickly from the vast Web log file, such as frequent access paths, frequent access page groups and user clustering. Through Web Usage Mining, several information left by user access can be mined which will provide foundation for decision making of organizations, Also the process of Web mining was defined as the set of techniques designed to explore, process and analyze large masses of consecutive information activities on the Internet, has three main steps: data preprocessing, extraction of reasons of the use and the interpretation of results. This paper will start with the presentation of different formats of web log files, then it will present the different preprocessing method that have been used, and finally it presents a system for “Web content and Usage Mining'' for web data extraction and web site analysis using Data Mining Algorithms Apriori, FPGrowth, K-Means, KNN, and ID3.

scholarly journals A Lime Light on the Emerging Trends of Web Mining

2013 ◽  
pp. 225-230
Author(s):  
Udayasri. B ◽  
Sushmitha. N ◽  
Padmavathi. S
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Secondary Data ◽  
Web Page ◽  
Hidden Information ◽  
Useful Knowledge ◽  
The Past ◽  
Emerging Trends ◽  
The World ◽  
Information Center

The World Wide Web is a huge, information center for a variety of applications. Web contains a dynamic and rich collection of hyperlink information. It allows Web page access, usage of information and provides numerous sources for data mining. The goal of Web mining is to discover the pattern of access and hidden information from huge collections of documents. In this paper we are presenting the various emerging web mining techniques that are effectively efficient in overcoming the demerits of existing technologies and also give the superficial knowledge and comparison about data mining. This paper describes the past, current and future of web mining. Web mining attempts to determine useful knowledge from secondary data obtained from the interactions of the users with the web. We have also described the personalization on web which is used to manipulate the information presented to the users through the various personalization strategies.

scholarly journals Web Page Recommendation using Random Forest with Fire Fly Algorithm in Web Mining

2020 ◽  
Vol 9 (3) ◽  
pp. 499-505
Keyword(s):  
Web Mining ◽  
Heuristic Algorithms ◽  
Research Work ◽  
Web Pages ◽  
Clustering Methods ◽  
Web Page ◽  
Continuous Growth ◽  
Log Files ◽  
Speed Up ◽  
The Web

Nowadays, internet has become the easiest way to obtain more information from the web and millions of users search internet to find out the information. The continuous growth of web pages and users interest to search more information about various topics increases the complexity of recommendation. The user's behavior is extracted by using the web mining techniques, which are used in web server log. The main aim of this research study is to identify the navigation pattern of users from the log files. There are three major steps in the web mining process namely pre-processing the data, classification of pattern and users discovery. In recent periods, the web page articles are classified by the researchers before recommending the requested page to users. However, every category size is too large or manual labors are often needed for classification tasks. A high time complexity issues are faced by some existing clustering methods or according to the initial parameters, these techniques provides the iterative computing that leads to insufficient results. To address the above issues, a recommendation for web page is developed by initializing the margin parameters of classification techniques which considers both effectiveness and efficiency. This research work initializes the Random Forest's (RF) margin parameters by using the FireFly Algorithm (FFA) for reducing the processing time to speed up the process. A large volume of user's interest data is processed by these margin parameters, which provides a better recommendation than existing techniques. The experimental results show that RF-FFA method achieved 41.89% accuracy and recall values, when compared with other heuristic algorithms.

Web Usage Mining with Web Logs

2011 ◽  
pp. 2096-2102 ◽  
Author(s):  
Xiangji Huang
Keyword(s):  
Web Mining ◽  
Web Search ◽  
Relevant Information ◽  
Web Usage Mining ◽  
Log Data ◽  
Web Log ◽  
Web Usage ◽  
Web Access ◽  
Navigation Patterns ◽  
Access Logs

With the rapid growth of the World Wide Web, the use of automated Web-mining techniques to discover useful and relevant information has become increasingly important. One challenging direction is Web usage mining, wherein one attempts to discover user navigation patterns of Web usage from Web access logs. Properly exploited, the information obtained from Web usage log can assist us to improve the design of a Web site, refine queries for effective Web search, and build personalized search engines. However, Web log data are usually large in size and extremely detailed, because they are likely to record every aspect of a user request to a Web server. It is thus of great importance to process the raw Web log data in an appropriate way, and identify the target information intelligently. In this chapter, we first briefly review the concept of Web Usage Mining and discuss its difference from classic Knowledge Discovery techniques, and then focus on exploiting Web log sessions, defined as a group of requests made by a single user for a single navigation purpose, in Web usage mining. We also compare some of the state-of-the-art techniques in identifying log sessions from Web servers, and present some popular Web mining techniques, including Association Rule Mining, Clustering, Classification, Collaborative Filtering, and Sequential Pattern Learning, that can be exploited on the Web log data for different research and application purposes.

Web Page Recommendation Using Genetic and Feed Forward Association Rule on Web-Log Features

2020 ◽  
Vol 17 (9) ◽  
pp. 4462-4467
Author(s):  
B. Pavithra ◽  
M. Niranjanamurthy
Keyword(s):  
Association Rule ◽  
Web Mining ◽  
Recommendation System ◽  
User Behavior ◽  
Web Page ◽  
User Behavior Analysis ◽  
Feed Forward ◽  
Web Log ◽  
Fitness Value ◽  
Forward Association

As websites are increasing day by day, so user behavior analysis for improving the website performance attracts many researcher. This paper introduces the web page recommendation model using web log feature of web mining. Here work has introduce Feed forward counter model (FFC) for identifying the association rule with single data iteration technique. Hence execution time for this gets reduced. Work has introduced the Particle swarm optimization algorithm for the selection of appropriate page from given user path as recommendation page. This work involves support of the association rule as fitness value. Experiment was done on real dataset obtained from project tunnel website. Results shows that by the use of Feed forward association rule with PSO for next page recommendation system has improve various evaluation parameters like precision, coverage, m-metric.

scholarly journals An Integrated Approach for Knowledge discovery and Information retrieval on Web

2020 ◽  
pp. 81-83
Keyword(s):  
Data Mining ◽  
World Wide Web ◽  
Knowledge Discovery ◽  
Web Mining ◽  
World Wide ◽  
Information Filtering ◽  
Integrated Approach ◽  
Web Data ◽  
Web Information ◽  
The World

This Paper focuses on the integration of web information and subsequent knowledge relationship discovery within the integrated web data. The problem of information overload on the Internet has brought new attention to the ideas of filtering information on internet. Knowledge Discovery is often used for analysis of large amounts of web data and enables addressing a number of tasks that arise in Semantic Web and require scalable solutions. The World Wide Web and related web Information resources no arguably stand as the best-preferred medium for distributing information. It introduces various approaches to knowledge relation discovery like model creation, exact comparison and dynamic comparison. The nature of the web and the mass of valuable web information it holds, poses an ideal stage for applying data mining techniques for efficient discovery of knowledge from the World Wide Web. The eagerness shown by various research communities has made web based data mining (Web Mining) a rich mixture of different technologies. Therefore the heterogeneity in the area of web mining is as high as web itself. Our objective is to design an approach for information filtering, a general approach to personalized information filtering. Social Information filtering essentially automates the process of “word-of-mouth” recommendations: items are recommended to a user based upon values assigned by other people with similar taste. The system determines which users have similar taste via standard formulas for computing statistical correlations. The World Wide Web (WWW) provides a vast source of information. Technique for making personalized recommendations from any type of database to a user based on similarities between the interest profile of that user and those of other users. Recent years have seen the explosive growth of the sheer volume of information.

New species list of plants and lichens of Russia for Turboveg for Windows

2020 ◽  
pp. 151-156
Author(s):  
A. P. Korablev ◽  
N. S. Liksakova ◽  
D. M. Mirin ◽  
D. G. Oreshkin ◽  
P. G. Efimov
Keyword(s):  
New Species ◽  
Vascular Plants ◽  
Russian Literature ◽  
Web Page ◽  
Species List ◽  
Internet Resources ◽  
The World ◽  
A New Species ◽  
The Web

A new species list of plants and lichens of Russia and neighboring countries has been developed for Turboveg for Windows, the program, intended for storage and management of phytosociological data (relevés), is widely used all around the world (Hennekens, Schaminée, 2001; Hennekens, 2015). The species list is built upon the database of the Russian website Plantarium (Plantarium…: [site]), which contains a species atlas and illustrated an online Handbook of plants and lichens. The nomenclature used on Plantarium was originally based on the following issues: vascular plants — S. K. Cherepanov (1995) with additions; mosses — «Flora of mosses of Russia» (Proect...: [site]); liverworts and hornworts — A. D. Potemkin and E. V. Sofronova (2009); lichens — «Spisok…» G. P. Urbanavichyus ed. (2010); other sources (Plantarium...: [site]). The new species list, currently the most comprehensive in Turboveg format for Russia, has 89 501 entries, including 4627 genus taxa compare to the old one with 32 020 entries (taxa) and only 253 synonyms. There are 84 805 species and subspecies taxa in the list, 37 760 (44.7 %) of which are accepted, while the others are synonyms. Their distribution by groups of organisms and divisions are shown in Table. A large number of synonyms in the new list and its adaptation to work with the Russian literature will greatly facilitate the entry of old relevé data. The ways of making new list, its structure as well as the possibilities of checking taxonomic lists on Internet resources are considered. The files of the species list for Turboveg 2 and Turboveg 3, the technique of associating existing databases with a new species list (in Russian) are available on the web page https://www.binran.ru/resursy/informatsionnyye-resursy/tekuschie-proekty/species_list_russia/.

Regenerative flow pumps, blowers and compressors – A review

2021 ◽  
pp. 095765092110181
Author(s):  
M Sreekanth ◽  
R Sivakumar ◽  
M Sai Santosh Pavan Kumar ◽  
K Karunamurthy ◽  
MB Shyam Kumar ◽  
...  
Keyword(s):  
Quantitative Data ◽  
Research Work ◽  
Future Research ◽  
Performance Parameters ◽  
The World ◽  
Working Principle ◽  
Computational Fluid Dynamics Cfd ◽  
Specific Speed ◽  
Regenerative Flow ◽  
Product Description

This paper presents a detailed and objective review of regenerative flow turbomachines, namely pumps, blowers and compressors. Several aspects of turbomachines like design and operating parameters, working principle, flow behaviour, performance parameters and analytical and Computational Fluid Dynamics (CFD) related details have been reviewed and summarized. Experimental work has been put in perspective and the most useful results for optimized performance have been presented. Consolidated plots of specific speed-specific diameter have been plotted which can be helpful in the early stages of design. Industrial outlook involving details of suppliers from various parts of the world, their product description and applications too are included. Finally, future research work to be carried out to make these machines widespread is suggested. This review is targeted at designer engineers who would need quantitative data to work with.

The Research of the Accessibility Website Design Based on Web Log Mining

2012 ◽  
Vol 241-244 ◽  
pp. 2779-2782
Author(s):  
Heng Yao Tang ◽  
Xiao Yan Zhan
Keyword(s):  
Website Design ◽  
Web Log Mining ◽  
Web Page ◽  
Web Log ◽  
User Interests ◽  
Mining Technique ◽  
Dynamic Web ◽  
Log Mining ◽  
The Web

On the problems existing in the realization of current accessibility website, we design a web designing architecture, using the web log mining technique to extract user interests and access priority sequence and adopting the dynamic web page information to fill the web page commonly used structure, realize the intelligent , personalized accessibility.

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