scholarly journals Ontology Based Data Mining Approach on Web Documents

2014 ◽  
Vol 7 (4) ◽  
pp. 123
Author(s):  
Hamideh Hajiabadi
Keyword(s):  
Data Mining ◽  
Data Sources ◽  
Text Documents ◽  
Web Documents ◽  
Valuable Data ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
Huge Data ◽  
Data Source ◽  
Extract Information

Internet which is included plenty of huge data source is now rapidly increasing in all domains. It is considered as valuable data sources if the data can be processed that results in information. Data mining techniques are widely utilized in web documents in order to extract information. In this paper a data mining approach based on Ontology is proposed to classify web documents in order to facilitate applications based on classified text documents like search engines. The proposed approach is implemented and applied on several web documents. The experimental results show considerable progress.

Trends in Web Content and Structure Mining

2011 ◽  
pp. 1146-1150
Author(s):  
Anita Lee-Post ◽  
Haihao Jin
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Web Content ◽  
Web Documents ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Recent Interest ◽  
The Web ◽  
Extract Information

Web mining is the use of data mining techniques to automatically discover and extract information from Web documents and services. This area of research is fast-developing today, drawing attention and interests from both researchers and practitioners. The tremendous growth of information available on the Web and the recent interest in e-commerce have accounted for this phenomenon (Kosala & Blockeel, 2000).

A Data Mining Approach for Risk Assessment in Car Insurance

2014 ◽  
Vol 5 (3) ◽  
pp. 11-28
Author(s):  
Ljiljana Kašćelan ◽  
Vladimir Kašćelan ◽  
Milijana Novović-Burić
Keyword(s):  
Data Mining ◽  
Risk Assessment ◽  
Risk Premium ◽  
Study Data ◽  
Functional Dependencies ◽  
Standard Methods ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
The One ◽  
Car Insurance

This paper has proposed a data mining approach for risk assessment in car insurance. Standard methods imply classification of policies to great number of tariff classes and assessment of risk on basis of them. With application of data mining techniques, it is possible to get functional dependencies between the level of risk and risk factors as well as better results in predictions. On the case study data it has been proved that data mining techniques can, with better accuracy than the standard methods, predict claim sizes and occurrence of claims, and this represents the basis for calculation of net risk premium and risk classification. This paper, also, discusses advantages of data mining methods compared to standard methods for risk assessment in car insurance, as well as the specificities of the obtained results due to small insurance market, such is the one in Montenegro.

scholarly journals ANALYSIS OF THESIS ADVISING PATTERN IN MASTER PROGRAM ON FACULTY OF AGRICULTURE IPB USING DATA MINING APPROACH

Edulib ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. 194
Author(s):  
Lilis Syarifah ◽  
Imas Sukaesih Sitanggang ◽  
Pudji Muljono
Keyword(s):  
Data Mining ◽  
Upland Rice ◽  
Future Research ◽  
Rule Mining ◽  
Related Item ◽  
Text Documents ◽  
Master Program ◽  
R Language ◽  
Data Mining Approach ◽  
Using Data

The thesis is student study report which is accomplished as a requirement of graduation for Master program. Selecting study’s topic and advisors influence implementation of the study. Therefore, study’s topic is able to improve academic institution quality, however a large number of thesis documents on the repository cause difficulty to get information related to advisor’s expertness and the frequent or rare topic is former studied. Association rule mining can be used to mine information on the related item. This study aims to analyze advising patterns system in Master program on Agriculture based on supervisors and their topic research on metadata thesis of IPB repository and text documents of summary using data mining approach. The datas were collected from the repository of Bogor Agricultural University website and processed using R language programming. Pattern result of the reseach were that the most popular association on supervisor was occurred at support value of 0.00793 or equivalent to 7 theses and four popular topics were Botanical insecticide, Global warming, Upland Rice, and Land Use Change. The analysis result could be useful information to be reference or suggest future research or appropriate supervisor among agricultural.

scholarly journals The comparative study of text documents clustering algorithms

2015 ◽  
Vol 16 (SE) ◽  
pp. 133-138
Author(s):  
Mohammad Eiman Jamnezhad ◽  
Reza Fattahi
Keyword(s):  
Data Mining ◽  
Dna Analysis ◽  
Clustering Algorithms ◽  
Research Area ◽  
Large Set ◽  
Text Documents ◽  
Web Documents ◽  
Significant Research ◽  
The Comparative Study ◽  
F Measure

Clustering is one of the most significant research area in the field of data mining and considered as an important tool in the fast developing information explosion era.Clustering systems are used more and more often in text mining, especially in analyzing texts and to extracting knowledge they contain. Data are grouped into clusters in such a way that the data of the same group are similar and those in other groups are dissimilar. It aims to minimizing intra-class similarity and maximizing inter-class dissimilarity. Clustering is useful to obtain interesting patterns and structures from a large set of data. It can be applied in many areas, namely, DNA analysis, marketing studies, web documents, and classification. This paper aims to study and compare three text documents clustering, namely, k-means, k-medoids, and SOM through F-measure.

A Systematic Review About Prediction of Academic Behavior Through Data Mining Techniques

2020 ◽  
Vol 17 (11) ◽  
pp. 5162-5166
Author(s):  
Puninder Kaur ◽  
Amandeep Kaur ◽  
Rajwinder Kaur
Keyword(s):  
Systematic Review ◽  
Data Mining ◽  
Data Analysis ◽  
Educational Data Mining ◽  
Prediction Methods ◽  
Time Data ◽  
Academic Behavior ◽  
Data Mining Techniques ◽  
Real Time Data ◽  
Extract Information

In the IT world, predicting the academic performance of the huge student population poses a big challenge. Educational data mining techniques significantly contribute in providing solution to this problem. There are several prediction methods available for data classification and clustering, to extract information and provide accurate results. In this paper, different prediction methodologies are highlighted for the prediction of real-time data analysis of dynamic academic behavior of the students. The main focus is to provide brief knowledge about all data mining techniques and highlight dissimilarities among various methods in order to provide the best results for the students.

Informational Data Mining

2013 ◽  
pp. 58-65
Author(s):  
Feyza Gürbüz ◽  
Fatma Gökçe Önen
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Knowledge Discovery ◽  
Major Change ◽  
Research Community ◽  
Data Sources ◽  
Accurate Information ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Information Strategies

The previous decades have witnessed major change within the Information Systems (IS) environment with a corresponding emphasis on the importance of specifying timely and accurate information strategies. Currently, there is an increasing interest in data mining and information systems optimization. Therefore, it makes data mining for optimization of information systems a new and growing research community. This chapter surveys the application of data mining to optimization of information systems. These systems have different data sources and accordingly different objectives for knowledge discovery. After the preprocessing stage, data mining techniques can be applied on the suitable data for the objective of the information systems. These techniques are prediction, classification, association rule mining, statistics and visualization, clustering and outlier detection.

A Data Mining Approach for Risk Assessment in Car Insurance

Big Data ◽  
2016 ◽  
pp. 2028-2046
Author(s):  
Ljiljana Kašćelan ◽  
Vladimir Kašćelan ◽  
Milijana Novović-Burić
Keyword(s):  
Data Mining ◽  
Risk Assessment ◽  
Risk Premium ◽  
Study Data ◽  
Functional Dependencies ◽  
Standard Methods ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
The One ◽  
Car Insurance

This paper has proposed a data mining approach for risk assessment in car insurance. Standard methods imply classification of policies to great number of tariff classes and assessment of risk on basis of them. With application of data mining techniques, it is possible to get functional dependencies between the level of risk and risk factors as well as better results in predictions. On the case study data it has been proved that data mining techniques can, with better accuracy than the standard methods, predict claim sizes and occurrence of claims, and this represents the basis for calculation of net risk premium and risk classification. This paper, also, discusses advantages of data mining methods compared to standard methods for risk assessment in car insurance, as well as the specificities of the obtained results due to small insurance market, such is the one in Montenegro.

scholarly journals Data Mining as Support to Knowledge Management in Marketing

2015 ◽  
Vol 6 (2) ◽  
pp. 18-30 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Adela Has
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Knowledge Management ◽  
Association Rules ◽  
Marketing Strategies ◽  
Web Based ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
Model Generalization ◽  
Retail Managers

Abstract Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers’ profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in proposing an integrative data mining approach that could present support to knowledge management. The research could be useful to marketing and retail managers in improving the process of their decision making, as well as to researchers in the area of marketing modelling. Future studies should include more samples and other data mining techniques in order to test the model generalization ability.

Research and Application in the Web Data Mining Based on the XML

2014 ◽  
Vol 644-650 ◽  
pp. 2124-2127
Author(s):  
Fen Liu
Keyword(s):  
Data Mining ◽  
Information Transmission ◽  
Rapid Development ◽  
Data Sources ◽  
The Internet ◽  
Web Data ◽  
Web Data Mining ◽  
Mining Technology ◽  
Web Documents ◽  
The Web

With the rapid development of Internet, the Internet has become the important resources of information transmission and share. The characteristics of Web data are semi-structured, heterogeneous and mass, making traditional data mining technology indirectly applied to Web data sources. Web data mining refers to extracting a potential, useful model from the Web documents or Web activities. Because of the structural and expansibility of XML, research on XML combined with Web data mining has also became popular.

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