A Model for Extracting Most Desired Web Pages

2020 ◽  
pp. 119-145
Author(s):  
Jayanti Mehra ◽  
Ramjeevan Singh Thakur
Keyword(s):  
Data Mining ◽  
Path Length ◽  
Web Mining ◽  
Statistical Information ◽  
Web Pages ◽  
Systems Practice ◽  
Clustering And Classification ◽  
Using Data ◽  
Access Logs ◽  
Manipulation Process

Weblog analysis takes raw data from access logs and performs study on this data for extracting statistical information. This info incorporates a variety of data for the website activity such as average no. of hits, total no. of user visits, failed and successful cached hits, average time of view, average path length over a website; analytical information such as page was not found errors and server errors; server information, which includes exit and entry pages, single access pages, and top visited pages; requester information like which type of search engines is used, keywords and top referring sites, and so on. In general, the website administrator uses this kind of knowledge to make the system act better, helping in the manipulation process of site, then also forgiving marketing decisions support. Most of the advanced web mining systems practice this kind of information to take out more difficult or complex interpretations using data mining procedures like association rules, clustering, and classification.

Institutional Research Using Data Mining

2011 ◽  
pp. 66-102 ◽  
Author(s):  
Constanta-Nicoleta Bodea ◽  
Vasile Bodea ◽  
Radu Mogos
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Virtual Learning Environment ◽  
Student Activities ◽  
Participatory Behavior ◽  
Student Profiles ◽  
Clustering And Classification ◽  
E Learning ◽  
Using Data ◽  
And Behavior

The aim of this chapter is to explore the application of data mining for analyzing academic performance in connection with the participatory behavior of the students enrolled in an online two-year Master degree program in project management. The main data sources were the operational database with the students’ records and the log files and statistics provided by the e-learning platform. One hundred eighty-one enrolled students, and more than 150 distinct characteristics/ variables per student were used. Due to the large number of variables, an exploratory data analysis through data mining was chosen, and a model-based discovery approach was designed and executed in Weka environment. The association rules, clustering, and classification were applied in order to identify the factors explaining the students’ performance and the relationship between academic performance and behavior in the virtual learning environment. Data mining has revealed interesting patterns in data. These patterns indicate that academic performance is related to the intensity of the student activities in virtual environment. If the student understands how to work and she/he is motivated to communicate with others, then he might have a good academic performance. Based on clustering analysis, different student profiles were discovered, explaining the academic performance. The results are very encouraging and suggest several future developments.

FINDING REPRESENTATIVE WEB PAGES BASED ON A SOM AND A REVERSE CLUSTER ANALYSIS

2011 ◽  
Vol 20 (01) ◽  
pp. 93-118 ◽  
Author(s):  
SEBASTIÁN A. RÍOS ◽  
JUAN D. VELÁSQUEZ
Keyword(s):  
Cluster Analysis ◽  
Web Site ◽  
Web Mining ◽  
Mining Area ◽  
Web Pages ◽  
Feature Map ◽  
Data Mining Algorithms ◽  
Web Text Mining ◽  
Using Data ◽  
Self Organizing

Enhancing the content and structure of a web site is a very important task which can help to maintain people visiting a web site and gain new visits (or customers). Web mining area helps to enhance a web site organization and contents using data mining algorithms. In particular we may perform Web Mining using a Self Organizing Feature Map (SOFM or SOM) it is always needed an analysis phase by experts. To help analysts to perform this phase after SOFMs' training, many post-processing techniques have been developed (component planes, labels, etc.); however, none of these techniques are useful when working in web mining for off-line enhancements of a web site. In this paper an algorithm called Reverse Cluster Analysis (RCA) will be provided. It aims to identify important web pages based on a self organizing feature map (SOFM) when performing web text mining (WTM) and web usage mining (WUM). We successfully applied this technique in a real web site to show its effectiveness. We have extended previous work performing a comparison with another unsupervised technique, administrators survey and an extended survey.

Web Pages Classification with Parliamentary Optimization Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 499-513 ◽  
Author(s):  
Soner Kiziloluk ◽  
Ahmet Bedri Ozer
Keyword(s):  
Optimization Algorithm ◽  
Web Mining ◽  
Web Pages ◽  
Data Sets ◽  
Web Page ◽  
Web Documents ◽  
Web Page Classification ◽  
Using Data ◽  
Extract Information ◽  
Page Classification

In recent years, data on the Internet has grown exponentially, attaining enormous dimensions. This situation makes it difficult to obtain useful information from such data. Web mining is the process of using data mining techniques such as association rules, classification, clustering, and statistics to discover and extract information from Web documents. Optimization algorithms play an important role in such techniques. In this work, the parliamentary optimization algorithm (POA), which is one of the latest social-based metaheuristic algorithms, has been adopted for Web page classification. Two different data sets (Course and Student) were selected for experimental evaluation, and HTML tags were used as features. The data sets were tested using different classification algorithms implemented in WEKA, and the results were compared with those of the POA. The POA was found to yield promising results compared to the other algorithms. This study is the first to propose the POA for effective Web page classification.

Application of Conventional Data Mining Techniques and Web Mining to Aid Disaster Management

2019 ◽  
pp. 549-578
Author(s):  
Akshay Kumar ◽  
Alok Bhushan Mukherjee ◽  
Akhouri Pramod Krishna
Keyword(s):  
Data Mining ◽  
Disaster Management ◽  
Web Mining ◽  
Geometric Mean ◽  
Landslide Hazard ◽  
Data Mining Techniques ◽  
Landslide Event ◽  
Using Data ◽  
Artificial Neural Network Ann ◽  
Fuzzy Geometric Mean

Data mining techniques have potential to unveil the complexity of an event and yields knowledge that can create a difference. They can be employed to investigate natural phenomena; since these events are complex in nature and are difficult to characterize as there are elements of uncertainty involved in their functionality. Therefore, techniques that are compatible with uncertain elements can be employed to study them. This chapter explains the concepts of data mining and discusses at length about the landslide event. Further, the utility of data mining techniques in disaster management using a previous work was explained and provides a brief note on the efficiency of web mining in creating awareness about natural hazard by providing refined information. Finally, a conceptual framework for landslide hazard assessment using data mining techniques such as Artificial Neural Network (ANN), Fuzzy Geometric Mean Model (FGMM), etc. were chosen for description. It was quite clear from the study that data mining techniques are useful in assessing and modelling different aspects of landslide event.

Application of Conventional Data Mining Techniques and Web Mining to Aid Disaster Management

2017 ◽  
pp. 138-167 ◽  
Author(s):  
Akshay Kumar ◽  
Alok Bhushan Mukherjee ◽  
Akhouri Pramod Krishna
Keyword(s):  
Data Mining ◽  
Disaster Management ◽  
Web Mining ◽  
Geometric Mean ◽  
Landslide Hazard ◽  
Data Mining Techniques ◽  
Landslide Event ◽  
Using Data ◽  
Artificial Neural Network Ann ◽  
Fuzzy Geometric Mean

Data mining techniques have potential to unveil the complexity of an event and yields knowledge that can create a difference. They can be employed to investigate natural phenomena; since these events are complex in nature and are difficult to characterize as there are elements of uncertainty involved in their functionality. Therefore, techniques that are compatible with uncertain elements can be employed to study them. This chapter explains the concepts of data mining and discusses at length about the landslide event. Further, the utility of data mining techniques in disaster management using a previous work was explained and provides a brief note on the efficiency of web mining in creating awareness about natural hazard by providing refined information. Finally, a conceptual framework for landslide hazard assessment using data mining techniques such as Artificial Neural Network (ANN), Fuzzy Geometric Mean Model (FGMM), etc. were chosen for description. It was quite clear from the study that data mining techniques are useful in assessing and modelling different aspects of landslide event.

A Bluetooth User Positioning System for Locating, Informing, and Extracting Information using Data Mining Techniques

2011 ◽  
pp. 132-155
Author(s):  
John Garofalakis ◽  
Christos Mettouris
Keyword(s):  
Data Mining ◽  
Statistical Information ◽  
Positioning System ◽  
Positioning Systems ◽  
Data Mining Techniques ◽  
Location Data ◽  
Specialized Software ◽  
Positioning Method ◽  
Software And Hardware ◽  
Using Data

Until now, user positioning systems were focused mainly on providing users with exact location information. This makes them computational heavy while often demanding specialized software and hardware from mobile devices. In this paper we present a new user positioning system. The system is intended for use with m-commerce, by sending informative and advertising messages to users, after locating their position indoors. It is based exclusively on Bluetooth. The positioning method we use, while efficient is nevertheless simple. The m-commerce based messages, can be received without additional software or hardware installed. Moreover, the location data collected by our system are further processed using data mining techniques, in order to provide statistical information. After discussing the available technologies and methods for implementing indoor user positioning applications, we shall focus on implementation issues, as well as the evaluation of our system after testing it. Finally, conclusions are extracted.

scholarly journals Education is An Overview of Data Mining and The Ability to Predict the Performance of Students

Edukasi ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 19-28
Author(s):  
Mahjouba Ali Saleh ◽  
Sellappan Palaniappan ◽  
Nasaraldeen Ali Alghazali Abdalla
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Web Mining ◽  
Educational Data Mining ◽  
Research Area ◽  
Tutoring Systems ◽  
Educational Task ◽  
E Learning ◽  
Predicting Student Performance ◽  
Using Data

This research provides a review of the state of the art with respect to EDM and discusses the most relevant work in this area to date. Each study has been discussed considering type of data and data mining techniques used, and the kind of the educational task that they resolve. EDM is upcoming research area related to well-established areas of research such as e- learning, tutoring systems, web mining, data mining. Current literature show how fast educational data analysis area is growing and there is an increasing number of contributions that publish in International Journals and Conferences every year. However, educational data mining is still not a mature area. Some interesting future suggestion to develop this area has been presented. This research is a presentation of current and ancient literature of Predicting Student Performance using Data Mining.

Sign in / Sign up

Export Citation Format

Share Document