Survey of Collaborative Data Mining

Abstract In the information industry there is a huge amount of data which can be used to obtain relevant information necessary for them to be processed, cleaned and analyzed. This survey paper presents different data extraction research that can be applied in various areas. The main point is to determine a new approach and to be the starting point for new experiments in agriculture predictions. The collaborative data mining approach assumes that correlated units or devices will have similar behavior that can be determined with an acceptable approximation.

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Predicting Auditor Changes Using Financial Distress Variables And The Multiple Criteria Linear Programming (MCLP) And Other Data Mining Approaches

Journal of Applied Business Research (JABR) ◽

10.19030/jabr.v27i5.5597 ◽

2011 ◽

Vol 27 (5) ◽

pp. 73 ◽

Cited By ~ 2

Author(s):

Wikil Kwak ◽

Susan Eldridge ◽

Yong Shi ◽

Gang Kou

Keyword(s):

Data Mining ◽

Linear Programming ◽

Financial Distress ◽

Multiple Criteria ◽

Financial Statement ◽

Font Size ◽

Explanatory Variables ◽

Data Mining Approach ◽

Starting Point ◽

Accuracy Rates

<h1 style="margin: 0in 0.5in 0pt; text-align: justify; page-break-after: auto; mso-pagination: none;">Our study evaluates a multiple criteria linear programming (MCLP) and other data mining approaches to predict auditor changes using a portfolio of financial statement measures to capture financial distress. The results of the MCLP approach and the other data mining approaches show that these methods perform reasonably well to predict auditor changes using financial distress variables. Overall accuracy rates are more than 60 percent, and true positive rates exceed 80 percent. Our study is designed to establish a starting point for auditor-change prediction using financial distress variables. Further research should incorporate additional explanatory variables and a longer study period to improve prediction rates.</h1>

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Explanation in OLAP Data Cubes

Journal of Information Technology Research ◽

10.4018/jitr.2014100105 ◽

2014 ◽

Vol 7 (4) ◽

pp. 63-78 ◽

Cited By ~ 1

Author(s):

Rahhal Errattahi ◽

Mohammed Fakir ◽

Fatima Zahra Salmam

Keyword(s):

Data Mining ◽

Association Rules ◽

Data Structures ◽

Relevant Information ◽

Multidimensional Data ◽

New Approach ◽

Data Cubes ◽

Multidimensional Data Structures ◽

Analytical Processing ◽

Interactive Data

OLAP is an important technology that offers a fast and interactive data navigation, it also provides tools to explore data cubes in order to extract interesting information from a multidimensional data structures. However, the OLAP exploration is done manually, without tools that could automatically extract relevant information from the cube. In addition OLAP is not capable of explaining relationships that could exist within data. This paper presents a new approach to coupling between data mining and online analytical processing. Its approach provides the explanation in OLAP data cubes by using the association rules between the inter-dimensional predicates. The mining process could be done by one of the two algorithms, Apriori and Fp-Growth, in which aggregate measures to calculate support and confidence are exploited. It also evaluates the interestingness of mined association rules according to the Lift criteria.

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Integrating search engines with data mining for customer‐oriented information search

Information Management & Computer Security ◽

10.1108/09685220210446597 ◽

2002 ◽

Vol 10 (5) ◽

pp. 242-254 ◽

Cited By ~ 3

Author(s):

Tim France ◽

Dave Yen ◽

Jyun‐Cheng Wang ◽

Chia‐Ming Chang

Keyword(s):

Data Mining ◽

Web Sites ◽

Information Search ◽

Search Engines ◽

World Wide ◽

Relevant Information ◽

Final Decision ◽

Limited Information ◽

New Approach ◽

The World

In recent years, the World Wide Web (WWW) has become incredibly popular in homes and offices alike. Consumers need to search for relevant information to help solve purchasing problems on various Web sites. Although there is no question that great numbers of WWW users will continue using search engines for information retrieval, consumers still hesitate before making a final decision, often because only rough and limited information about the products is made available. Consequently, consumers need the help of data mining in order to help them make informed decisions. Herein we propose a new approach to integrating a search engine with data mining in an effort to help support customer‐oriented information search action. This approach also illustrates how to reduce the consumer’s information search perplexity.

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Discovering behavioral patterns among air pollutants: A data mining approach

Enfoque UTE ◽

10.29019/enfoqueute.v9n4.411 ◽

2018 ◽

Vol 9 (4) ◽

pp. 168-179 ◽

Cited By ~ 2

Author(s):

Diana Arce ◽

Fernando Lima ◽

Marcos Patricio Orellana Cordero ◽

John Ortega ◽

Chester Sellers ◽

...

Keyword(s):

Data Mining ◽

Air Pollution ◽

Air Pollutants ◽

Complete Information ◽

Particulate Material ◽

Andean Region ◽

Data Mining Approach ◽

Industry Standard ◽

Starting Point ◽

Future Data

Air pollutants affect both human health and the environment. For this reason, environmental managers and urban planners focus their efforts in monitoring air pollution. In this context, complete information is required to support the decision-making process to improve the quality of life in urban zones. Hence, it is important to extract knowledge not only on concentration levels but associations between air pollutants. Based on the Cross-industry standard process for data mining, this paper presents an approach which leads to identify correlations and incidence between the most harmful pollutants in the Andean Region: Ozone, Carbon monoxide, Sulfur dioxide, Nitrogen dioxide and, Particulate material. This paper describes an experiment using a real dataset from a monitoring station in Cuenca, Ecuador located in the Andean region. The results show that the proposed approach is effective to extract knowledge useful to support the evaluation of air quality in urban zones. In addition, this approach provides a starting point for future data mining applications for the analysis of air pollution in the context of the Andean region.

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Data Mining Approach to Image Feature Extraction in Old Painting Restoration

Foundations of Computing and Decision Sciences ◽

10.2478/fcds-2013-0007 ◽

2013 ◽

Vol 38 (3) ◽

pp. 159-174

Author(s):

Joanna Gancarczyk ◽

Joanna Sobczyk

Keyword(s):

Data Mining ◽

Feature Vector ◽

Detection Algorithm ◽

Image Feature ◽

Complex Nature ◽

Data Mining Algorithm ◽

New Approach ◽

Art Restoration ◽

Data Mining Approach ◽

Universal Detection

Abstract In this paper a new approach to image segmentation was discussed. A model based on a data mining algorithm set on a pixel level of an image was introduced and implemented to solve the task of identification of craquelure and retouch traces in digital images of artworks. Both craquelure and retouch identification are important steps in art restoration process. Since the main goal is to classify and understand the cause of damage, as well as to forecast its further enlargement, a proper tool for a precise detection of the damaged area is needed. However, the complex nature of the pattern is a reason why a simple, universal detection algorithm is not always possible to be implemented. Algorithms presented in this work apply mining structures which depend of expandable set of attributes forming a feature vector, and thus offer an elastic structure for analysis. The result obtained by our method in craquelure segmentation was improved comparing to the results achieved by mathematical morphology methods, which was confirmed by a qualitative analysis.

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Survey on data mining approach for analysis and prediction of student performance

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.5.21135 ◽

2018 ◽

Vol 7 (4.5) ◽

pp. 467 ◽

Cited By ~ 2

Author(s):

Chala Simon ◽

Ybralem Bugusa

Keyword(s):

Data Mining ◽

Academic Performance ◽

Student Performance ◽

Quality Of Education ◽

Survey Paper ◽

Data Mining Approach ◽

Student Academic Performance

The quality of education is measured by the academic performance of students and the results they produce. Since the student academic performance is the made up of the environmental, psychological, socio-economic and other factors, it is challenging to measure the aca- demic performance of students. Such difficulties can be reduced by investigation of various factors that influence the student perfor- mance. Many researchers have been used different approaches to identifying the variables that help to predict students’ performance. This survey paper examines various data mining methodologies that have been used to analyze and predict students’ performance.

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