Fuzzy Relational Fixed Point Clustering

Brouwer, Roelof K.

doi:10.1080/18756891.2009.9727641

“…In future study, we shall be working on integrating (carefully) special education domain knowledge with the existed discretization algorithms so that we can determine some appropriate cut-points that may uncover more precious and hidden information to assist the LD diagnosis procedure without reproducing rules that might have already been known. Finally, it may worth trying to adjust the parameter settings of the two clustering algorithms used in this study or adopting other clustering methods [42,43] in the future to see if those make any difference to the rules generated.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Rough Sets as a Knowledge Discovery and Classification Tool for the Diagnosis of Students with Learning Disabilities

Wu¹,

Huang²,

Meng³

et al. 2011

IJCIS

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Due to the implicit characteristics of learning disabilities (LDs), the diagnosis of students with learning disabilities has long been a difficult issue. Artificial intelligence techniques like artificial neural network (ANN) and support vector machine (SVM) have been applied to the LD diagnosis problem with satisfactory outcomes. However, special education teachers or professionals tend to be skeptical to these kinds of black-box predictors. In this study, we adopt the rough set theory (RST), which can not only perform as a classifier, but may also produce meaningful explanations or rules, to the LD diagnosis application. Our experiments indicate that the RST approach is competitive as a tool for feature selection, and it performs better in term of prediction accuracy than other rulebased algorithms such as decision tree and ripper algorithms. We also propose to mix samples collected from sources with different LD diagnosis procedure and criteria. By pre-processing these mixed samples with simple and readily available clustering algorithms, we are able to improve the quality and support of rules generated by the RST. Overall, our study shows that the rough set approach, as a classification and knowledge discovery tool, may have great potential in playing an essential role in LD diagnosis.

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“…In future study, we shall be working on integrating (carefully) special education domain knowledge with the existed discretization algorithms so that we can determine some appropriate cut-points that may uncover more precious and hidden information to assist the LD diagnosis procedure without reproducing rules that might have already been known. Finally, it may worth trying to adjust the parameter settings of the two clustering algorithms used in this study or adopting other clustering methods [42,43] in the future to see if those make any difference to the rules generated.…”

Section: Tung-kuang Wu Shian-chang Huang Ying-ru Meng Wen-yau Lianmentioning

confidence: 99%

Rough Sets as a Knowledge Discovery and Classification Tool for the Diagnosis of Students with Learning Disabilities

Wu

¹

,

Huang

²

,

Meng

³

et al. 2011

International Journal of Computational Intelligence Systems

4

0

View full text Add to dashboard Cite

Due to the implicit characteristics of learning disabilities (LDs), the diagnosis of students with learning disabilities has long been a difficult issue. Artificial intelligence techniques like artificial neural network (ANN) and support vector machine (SVM) have been applied to the LD diagnosis problem with satisfactory outcomes. However, special education teachers or professionals tend to be skeptical to these kinds of black-box predictors. In this study, we adopt the rough set theory (RST), which can not only perform as a classifier, but may also produce meaningful explanations or rules, to the LD diagnosis application. Our experiments indicate that the RST approach is competitive as a tool for feature selection, and it performs better in term of prediction accuracy than other rulebased algorithms such as decision tree and ripper algorithms. We also propose to mix samples collected from sources with different LD diagnosis procedure and criteria. By pre-processing these mixed samples with simple and readily available clustering algorithms, we are able to improve the quality and support of rules generated by the RST. Overall, our study shows that the rough set approach, as a classification and knowledge discovery tool, may have great potential in playing an essential role in LD diagnosis.

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“…Clustering, partitioning data into sensible groupings according to measured or perceived intrinsic characteristics or similarity, is one of the most fundamental unsupervised data mining tasks 1,2,3,4 . In the past decades, clustering methods have been successfully applied in a variety of applications across a wide range of fields, including computer vision, system biology, and e-business, etc.…”

Section: Introductionmentioning

confidence: 99%

Clustering with Instance and Attribute Level Side Information

Wang¹,

Wu²,

Li³

2010

IJCIS

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Selecting a suitable proximity measure is one of the fundamental tasks in clustering. How to effectively utilize all available side information, including the instance level information in the form of pair-wise constraints, and the attribute level information in the form of attribute order preferences, is an essential problem in metric learning. In this paper, we propose a learning framework in which both the pair-wise constraints and the attribute order preferences can be incorporated simultaneously. The theory behind it and the related parameter adjusting technique have been described in details. Experimental results on benchmark data sets demonstrate the effectiveness of proposed method.

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Fuzzy Relational Fixed Point Clustering

Cited by 7 publications

References 22 publications

Rough Sets as a Knowledge Discovery and Classification Tool for the Diagnosis of Students with Learning Disabilities

Rough Sets as a Knowledge Discovery and Classification Tool for the Diagnosis of Students with Learning Disabilities

Rough Sets as a Knowledge Discovery and Classification Tool for the Diagnosis of Students with Learning Disabilities

Clustering with Instance and Attribute Level Side Information

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