Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals

Fan, Chin-Yuan; Fan, Pei-Shu; Chan, Te-Yi; Chang, Shu-Hao

doi:10.1016/j.eswa.2012.02.005

Cited by 54 publications

(25 citation statements)

References 11 publications

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“…The author used is SOM(self-organizing map). This algorithm yields two dimensional and irregular representation of the input records [31,32,34]. The experimental outcomes of this algorithm prove that this algorithm is vastly valid in pattern recognition and is computationally efficient from this it can be accomplished that MTS-ANN algorithm can be effectively useful to dynamic environment for data-mining troubles [33].…”

Section: Discussionmentioning

confidence: 88%

“…The unverified automation of the coloring allows us to nuance the attachment of a class [31]. The given paper [34] shows machine learning and data mining application for the prediction of drifts in technology skilled turnover rates of the employees. Then he used an algorithm which is the combination of two different algorithm i.e., SOM (self-organizing map) and BPN(back propagation neural network) [34].…”

Section: Neural Network and Data Mining In Information Technologymentioning

confidence: 99%

“…This algorithm combines the advantages of SOM and BPN which applied on the expose properties associated to turnover trends cluster. With the help of this algorithm we come to know that this algorithm is the best algorithm for finding out the turnover of employees and also showing the factors which involve in increasing the rate of the employees turnover [34]. The author predicts a new intelligent approach which will forecast the frequent growth of software which is based on the functional networks forecasting framework [37].…”

Section: Neural Network and Data Mining In Information Technologymentioning

confidence: 99%

“…The given paper [34] shows machine learning and data mining application for the prediction of drifts in technology skilled turnover rates of the employees. Then he used an algorithm which is the combination of two different algorithm i.e., SOM (self-organizing map) and BPN(back propagation neural network) [34]. This algorithm combines the advantages of SOM and BPN which applied on the expose properties associated to turnover trends cluster.…”

Section: Neural Network and Data Mining In Information Technologymentioning

confidence: 99%

See 3 more Smart Citations

Application of Data Mining Using Artificial Neural Network: Survey

Arif¹,

Alam²,

Hussain³

2015

IJDTA

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Section: Discussionmentioning

confidence: 88%

Section: Neural Network and Data Mining In Information Technologymentioning

confidence: 99%

Section: Neural Network and Data Mining In Information Technologymentioning

confidence: 99%

Section: Neural Network and Data Mining In Information Technologymentioning

confidence: 99%

See 2 more Smart Citations

Application of Data Mining Using Artificial Neural Network: Survey

Arif¹,

Alam²,

Hussain³

2015

IJDTA

View full text Add to dashboard Cite

show abstract

“…It includes two parallel approaches. One of the approaches is considering finding the similarity of the image bands width to attain the classification outcomes [20]. Figure 4b shows the similarity of image bands.…”

Section: Spectrum Similarity Analysismentioning

confidence: 99%

The Analysis on Similarity of Spectrum Analysis of Landslide and Bareland through Hyper-Spectrum Image Bands

Wan

Lei

Lin

et al. 2019

Water

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Landslides of Taiwan occur frequently in high mountain areas. Soil disturbance causes by the earthquake and heavy rainfall of the typhoon seasons often produced the earth and rock to landslide in the upper reaches of the catchment area. Therefore, the landslide near the hillside has an influence on the catchment area. The hyperspectral images are effectively used to monitor the landslide area with the spectral analysis. However, it is rarely studied how to interpret it in the image of the landslide. If there are no elevation data on the slope disaster, it is quite difficult to identify the landslide zone and the bareland area. More specifically, this study used a series of spectrum analysis to identify the difference between them. Therefore, this study conducted a spectrum analysis for the classification of the landslide, bareland, and vegetation area in the mountain area of NanXi District, Tainan City. On the other hand, this study used the following parallel study on Support Vector Machine (SVM) for error matrix and thematic map for comparison. The study simultaneously compared the differences between them. The spectral similarity analysis reaches 85% for testing data, and the SVM approach has 98.3%.

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