Towards supporting expert evaluation of clustering results using a data mining process model

Osei-Bryson, Kweku-Muata

doi:10.1016/j.ins.2009.09.019

Cited by 35 publications

(25 citation statements)

References 50 publications

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“…Several works reviewing cluster validation indexes have been published [6,[43][44][45]. These works and other using or defining new criteria are shown in Table 1.…”

Section: Clustering Validationmentioning

confidence: 99%

“…various classifications are compatible with the set of given patterns. For this reason, research in this area aims to develop suitable tools and models for selecting classifications [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Previous research in this direction uses selection criteria as filters: a set of criteria is applied sequentially to all the obtained classifications [6][7][8][9]. All those classifications failing to meet a particular criterion are discarded and not taken into account in the application of the subsequent criterion.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ranking and selection of unsupervised learning marketing segmentation

Sánchez-Hernández¹,

Chiclana²,

Agell³

et al. 2013

Knowledge-Based Systems

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“…Several works reviewing cluster validation indexes have been published [6,[43][44][45]. These works and other using or defining new criteria are shown in Table 1.…”

Section: Clustering Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ranking and selection of unsupervised learning marketing segmentation

Sánchez-Hernández¹,

Chiclana²,

Agell³

et al. 2013

Knowledge-Based Systems

View full text Add to dashboard Cite

“…Nowadays, data mining has given a great deal of concern and attention in the information industry and in society as a whole. This technique is an approach that is currently receiving great attention in data analysis and it has been recognized as a newly emerging analysis tool (Osei-Bryson, 2010;Park, 2001;Sinha, 2008;Tso & Yau, 2007;Wan, 2009;Zanakis, 2005;Zhuang et al, 2009). Additionally, among the major tasks in data mining are classification and prediction; concept description; rule association; cluster analysis; outlier analysis; trend and evaluation analysis; statistical analysis and others.…”

Section: Introductionmentioning

confidence: 99%

Data Mining Classification Techniques for Human Talent Forecasting

Jantan¹,

Hamdan²,

Othman³

2011

Knowledge-Oriented Applications in Data Mining

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“…Currently, this process has acquired a great deal of attention as well as concerns from the information industry and society. This technique is also receiving significant attention in data analysis as well as it been recognized as a newly emerging tool for analysis [4]. Predicting student performance is a very beneficial in providing students as well as lecturers with the necessary assistance in the learning process.…”

mentioning

confidence: 99%

Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis

Hamoud¹,

Hashim²,

Awadh³

2018

IJIMAI

135

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The overall success of educational institutions can be measured by the success of its students. Providing factors that increase success rate and reduce the failure of students is profoundly helpful to educational organizations. Data mining is the best solution to finding hidden patterns and giving suggestions that enhance the performance of students. This paper presents a model based on decision tree algorithms and suggests the best algorithm based on performance. Three built classifiers (J48, Random Tree and REPTree) were used in this model with the questionnaires filled in by students. The survey consists of 60 questions that cover the fields, such as health, social activity, relationships, and academic performance, most related to and affect the performance of students. A total of 161 questionnaires were collected. The Weka 3.8 tool was used to construct this model. Finally, the J48 algorithm was considered as the best algorithm based on its performance compared with the Random Tree and RepTree algorithms.

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Towards supporting expert evaluation of clustering results using a data mining process model

Cited by 35 publications

References 50 publications

Ranking and selection of unsupervised learning marketing segmentation

Ranking and selection of unsupervised learning marketing segmentation

Data Mining Classification Techniques for Human Talent Forecasting

Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis

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