A User-Centered Log-Based Information Retrieval System Using Web Log Mining

2017 ◽  
pp. 343-362
Author(s):  
Sathiyamoorthi V
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Production Control ◽  
Research Area ◽  
Web Page ◽  
Web Based ◽  
Average Speed ◽  
Layout Structure ◽  
Knowledge Discovery In Database ◽  
Recent Trends

It is generally observed throughout the world that in the last two decades, while the average speed of computers has almost doubled in a span of around eighteen months, the average speed of the network has doubled merely in a span of just eight months! In order to improve the performance, more and more researchers are focusing their research in the field of computers and its related technologies. Data Mining is also known as knowledge discovery in database (KDD) is one such research area. The discovered knowledge can be applied in various application areas such as marketing, fraud detection, customer retention and production control and marketing to improve their business. It discovers implicit, previously unknown and potentially useful information out of datasets. Recent trends in data mining include web mining where it discovers knowledge from web based information to improve the page layout, structure and its content thereby it reduces the user latency in accessing the web page and website performance.

Data Mining and Data Warehousing

2017 ◽  
pp. 312-337 ◽  
Author(s):  
Sathiyamoorthi V
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Data Warehousing ◽  
Research Area ◽  
Huge Amount ◽  
Web Based ◽  
Page Layout ◽  
Average Speed ◽  
Layout Structure ◽  
The World

It is generally observed throughout the world that in the last two decades, while the average speed of computers has almost doubled in a span of around eighteen months, the average speed of the network has doubled merely in a span of just eight months! In order to improve the performance, more and more researchers are focusing their research in the field of computers and its related technologies. Data Mining is one such research area. It extracts useful information the huge amount of data present in the database. The discovered knowledge can be applied in various application areas such as marketing, fraud detections and customer retention. It discovers implicit, previously unknown and potentially useful information out of datasets. Recent trend in data mining include web mining where it discover knowledge from web based information to improve the page layout, structure and its content.

Fundamentals of Data Mining and Data Warehousing

2017 ◽  
pp. 1-26
Author(s):  
Sathiyamoorthi V
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Data Warehousing ◽  
Fraud Detection ◽  
Web Based ◽  
Page Layout ◽  
Average Speed ◽  
Layout Structure ◽  
The World ◽  
Recent Trends

It is generally observed throughout the world that in the last two decades, while the average speed of computers has almost doubled in a span of around eighteen months, the average speed of the network has doubled merely in a span of just eight months! In order to improve the performance, more and more researchers are focusing their research in the field of computers and its related technologies. Data mining is one such area that extracts useful information from the huge amount of data present in the dataset. The discovered knowledge can be applied in various application areas such as marketing, fraud detection and customer retention in product based companies and so on. It discovers implicit, previously unknown and potentially useful information out of dataset. Recent trends in data mining include web mining where it discovers knowledge from web based information to improve the page layout, structure and its contents.

Survey on Optimization Algorithms Used for Feature Selection Techniques in Web Page Classification

2019 ◽  
Vol 16 (2) ◽  
pp. 384-388 ◽  
Author(s):  
K. S. Ramanujam ◽  
K. David
Keyword(s):  
Machine Learning ◽  
Web Mining ◽  
Research Area ◽  
Web Pages ◽  
Web Page ◽  
Web Page Classification ◽  
Significant Research ◽  
Multiple Classifier ◽  
The Web ◽  
Page Classification

Web page classification refers to one of the significant research are in the web mining domain. Enormous quantity of data existing in the web demands the essential development of various effective and robust techniques to undergo web mining task that involves the process to categorizing the web page based on the data labels. It also includes various other tasks such as web crawling, analysis of web links and contextual advertising process. Existing machine learning and data mining techniques are being efficiently used for various web mining processes which include classification of web pages. Using of multiple classifier techniques are most promising research area while considering machine learning that works on the base of merging various classifiers with difference in base classifier and/or dataset distribution. With this several classification models are constructed that is highly robust in nature. This review paper, comparison has been done between FA, PSO, ACO, GA and IWT, to evaluate best fit algorithm for classifying web pages.

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.

Research on Data Mining Technology in Web Based on the Cloud Computing

2012 ◽  
Vol 532-533 ◽  
pp. 919-923 ◽  
Author(s):  
Feng Zhang ◽  
Li Liu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Web Mining ◽  
Information Service ◽  
Great Difficulty ◽  
Web Based ◽  
The Web ◽  
Better Than ◽  
Web Knowledge

To improve the data mining efficiency, analyzed existing algorithm for data mining.However,it has some uncertain knoledge are a major concern in data mining, it is great difficulty for data mining in web knoledge,which contains more uncertainty than an affirmatory one dees. In this paper, with web mining method based on the cloud computing analysis. One is the main issues related to the web knowledge problem are detaled, the other is the commonly used methods of handling web knowledge problems in data mining are reviewed, with a diseussion about a number of their known strength and weakness. This can be used to improve the quality of information service on web and can assist the web master to optimize site architec and increase visiting efficiency. The results of experiment show that it is better than that of the existing methods proposed in the literature.

Chinese Web Page Classification Based on Vector Space Model

2013 ◽  
Vol 846-847 ◽  
pp. 1801-1804
Author(s):  
Li Wei ◽  
Ling Zhang ◽  
Hua Mei Li ◽  
Xiao Zhou Chen
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Vector Space ◽  
Vector Space Model ◽  
Research Area ◽  
Supervised Machine Learning ◽  
Web Page ◽  
Web Page Classification ◽  
Space Model ◽  
Page Classification

Chinese web page classification has been considered as a hot research area in data mining. In this paper, Chinese web page classification algorithm based on vector space model is proposed. This algorithm makes use of supervised machine learning theory to implement a web page classifier. It combined text frequency and methods for feature extraction and improved traditional TFIDF weighting formula. The results show that the classifier was feasible and effective.

scholarly journals Web Mining for the Integration of Data Mining with Business Intelligence in Web-Based Decision Support Systems

2015 ◽  
pp. 120-142 ◽  
Author(s):  
Marcos Aurélio Domingues ◽  
Alípio Mário Jorge ◽  
Carlos Soares ◽  
Solange Oliveira Rezende
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Web Mining ◽  
Support Systems ◽  
Web Documents ◽  
Web Based ◽  
Use Of Data ◽  
Computer Based

Web mining can be defined as the use of data mining techniques to automatically discover and extract information from web documents and services. A decision support system is a computer-based information system that supports business or organizational decision-making activities. Data mining and business intelligence techniques can be integrated in order to develop more advanced decision support systems. In this chapter, the authors propose to use web mining as a process to develop advanced decision support systems in order to support the management activities of a website. They describe the web mining process as a sequence of steps for the development of advanced decision support systems. By following such a sequence, the authors can develop advanced decision support systems, which integrate data mining with business intelligence, for websites.

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.

scholarly journals Practise Mining to Predict Type of Client Performence

2019 ◽  
Vol 8 (9S3) ◽  
pp. 964-968
Keyword(s):  
Data Mining ◽  
Information System ◽  
Business Process ◽  
Web Mining ◽  
Process Mining ◽  
Research Area ◽  
Customer Behavior ◽  
Innovative Research ◽  
Related Information ◽  
Event Log

The aim of process mining implement is firstly to discover the typical customer fulfillment business process-process mining act as a bridge between data mining and web mining. Process mining in an active innovative research area in recent year. The goal is to be extract process –related information from the event log by observing events recorded by some of the information system using the click stream method. Finally we are classifying the different categories of customer behavior using weka tool after we applied the knowledge miner. The result provides to find the different type of customer and their behavior and its helps the company to improve the product and satisfied customer needs.

Analisis Data Pembayaran Kredit Nasabah Bank Menggunakan Metode Data Mining

2013 ◽  
Vol 4 (1) ◽  
pp. 18-27
Author(s):  
Ira Melissa ◽  
Raymond S. Oetama
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Database

Data mining adalah analisis atau pengamatan terhadap kumpulan data yang besar dengan tujuan untuk menemukan hubungan tak terduga dan untuk meringkas data dengan cara yang lebih mudah dimengerti dan bermanfaat bagi pemilik data. Data mining merupakan proses inti dalam Knowledge Discovery in Database (KDD). Metode data mining digunakan untuk menganalisis data pembayaran kredit peminjam pembayaran kredit. Berdasarkan pola pembayaran kredit peminjam yang dihasilkan, dapat dilihat parameter-parameter kredit yang memiliki keterkaitan dan paling berpengaruh terhadap pembayaran angsuran kredit. Kata kunci—data mining, outlier, multikolonieritas, Anova

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