Speeding up Incomplete Data Analysis using Matrix-Represented Approximations

Soe, Thin Thin; Min, Myat Myat

doi:10.1109/snpd.2018.8441160

Cited by 2 publications

(8 citation statements)

References 13 publications

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“…The accuracy is lower than the traditional rough set approach whereas analyzing missing datasets with more missing values. The examining results and discussions were described in our previous work [11]. In this paper, we emphasize that the performance comparison between the two representations of missing values, "do not care" conditions and lost values.…”

Section: Matrix-represented Approximationsmentioning

confidence: 92%

“…In [11], the speeding up incomplete data analysis system using matrix-represented approximations was proposed. Moreover, by different thresholds, a set of experiments on datasets with different missing percent was implemented.…”

Section: Related Workmentioning

confidence: 99%

“…Firstly, the relation matrix RM of the incomplete decision table is generated based on the characteristic relation [11]. The relation matrix RM, an n n matrix representing K A , is And then, the induced diagonal matrix IDM is constructed through the relation matrix.…”

Section: Matrix-represented Approximationsmentioning

confidence: 99%

“…The speeding up incomplete data analysis system using matrix-represented approximations is proposed in [11]. This system enables to handle missing data within the acceptable time and speedup than the traditional method.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Temporary Climate Variability for The Period 1965-2016 in The Pedro Moncayo Canton, Ecuador

Quinteros-Carabalí

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et al. 2019

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Nowadays, the veracity related with data quality such as incomplete, inconsistent, vague or noisy data creates a major challenge to data mining and data analysis. Rough set theory presents a special tool for handling the incomplete and imprecise data in information systems. In this paper, rough set based matrix-represented approximations are presented to compute lower and upper approximations. The induced approximations are conducted as inputs for data analysis method, LERS (Learning from Examples based on Rough Set) used with LEM2 (Learning from Examples Module, Version2) rule induction algorithm. Analyzes are performed on missing datasets with "do not care" conditions and missing datasets with lost values. In addition, experiments on missing datasets with different missing percent by using different thresholds are also provided. The experimental results show that the system outperforms when missing data are characterized as "do not care" conditions than represented as lost values.

show abstract

Section: Matrix-represented Approximationsmentioning

confidence: 92%

Section: Related Workmentioning

confidence: 99%

Section: Matrix-represented Approximationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Temporary Climate Variability for The Period 1965-2016 in The Pedro Moncayo Canton, Ecuador

Quinteros-Carabalí

Gallo

Cuesta

et al. 2019

IJRE

View full text Add to dashboard Cite

show abstract

“…The speeding up incomplete data analysis system using matrix-represented approximations is proposed in [8]. This system enables to handle missing data within an acceptable time and speedup than the traditional method.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Missing Data Using Matrix-Characterized Approximations

Soe¹,

Min²

2019

Software Engineering Research, Management and Applications

Self Cite

View full text Add to dashboard Cite

Nowadays, the veracity related to data quality such as incomplete, inconsistent, vague or noisy data creates a major challenge to data mining and data analysis. Rough set theory presents a special tool for handling the incomplete and imprecise data in information systems. In this paper, rough set based matrixrepresented approximations are presented to compute lower and upper approximations. The induced approximations are conducted as inputs for data analysis method, LERS (Learning from Examples based on Rough Set) used with LEM2 (Learning from Examples Module, Version2) rule induction algorithm. Analyzes are performed on missing datasets with "do not care" conditions and missing datasets with lost values. In addition, experiments on missing datasets with different missing percent by using different thresholds are also provided. The experimental results show that the system outperforms when missing data are characterized as "do not care" conditions than represented as lost values.

show abstract

Speeding up Incomplete Data Analysis using Matrix-Represented Approximations

Cited by 2 publications

References 13 publications

Temporary Climate Variability for The Period 1965-2016 in The Pedro Moncayo Canton, Ecuador

Temporary Climate Variability for The Period 1965-2016 in The Pedro Moncayo Canton, Ecuador

Analysis of Missing Data Using Matrix-Characterized Approximations

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