Linden and Yarnold (2016) studies the application of data mining techniques in behavioural studies in healthcare industry.
This study describes how data mining technique can be implemented starting from data collection, data structuring, pruning and analysis.
This is relevant and unique for the topic ‘data mining’ because it evaluates different data mining algorithm in the healthcare research domain.
The evaluation of data mining algorithms is its unique contribution
The advantage of this study is that it provides a comparison of traditional techniques. However, this study does not provide a conclusion on the most effective algorithm for data mining.
This study aids to provide the best practice in choosing the data mining algorithms.
Goncalves et al. (2015) aims to study and compare studies using data mining techniques in order to develop processes and systems that will improve the quality of life.
The study establishes that quality of life questionnaire help towards collecting quality data for data mining and hence effective decision-making.
This study will aid towards understanding how data mining technique can be used in the health care industry for collecting reliable data for decision-making.
This study is unique in terms of establishing how data can be procured systematically as well as easily.
This study provides a systematic and easy way of procuring data. However, this study is limited to data collection techniques for data mining.
This study will aid towards understanding how data mining technique can be used in the health care industry.
Dey and Rautaray (2014) analyses several data mining techniques that is appropriate in the health care industry for healthcare decision support systems (HDSS).
The study establishes that C4.5 data mining technique is the most effective algorithm for HDSS.
This study, despite being specific to the Healthcare industry, is relevant to the topic because it analyses several data mining algorithm techniques.
The contribution of this study is unique because this focuses specifically on heart disease database.
The advantage of this study is that it establishes the effective data-mining algorithm. However, this is also the disadvantage because this study does not shed light upon other types of databases used in the healthcare industry.
It identifies C4.5 data mining technique as the best approach for analysis of heart disease database.
Zhou et al. (2016) conducts a study, which proposes an inexpensive and a low intrusive method for procurement of behaviour information using the data mining techniques.
This study also establishes that C4.5 decision tree algorithm is a poor method for this industry.
This study is relevant to the topic because it analyses several data mining algorithm techniques.
The unique contribution is the development of an effective approach to data mining.
The advantage is identification of an effective approach to data mining in this industry for evaluation and prediction of consumer behaviour. However, the major limitation of this research is that the approach developed is not tested commercially.
The curve description algorithm of data mining is an effective method for understanding the behaviour of the consumers towards using AC.
The study by Papalexakis et al. (2017) studies the effectiveness of Tensor, which a matrix data type.
The study establishes that when a data class is present with different set of variables, then tensor, with their data cube concept is able to capture the data effectively.
This study is highly relevant for the topic of Data Mining because the study evaluates tensor as a data type in data mining.
The uniqueness of this study is that it provides a comprehensive evaluation of the use of tensor and tensor decomposition as a tool to be used in data mining under practical context.
While the advantage of this research is that it helps to provide a comprehensive study on the advancement of this concept in the area of data mining and across various industries. However, this study does not evaluate the challenges of tensor data type and tensor decomposition technique in data mining.
It helps to provide a comprehensive study on the advancement of tensor in the area of data mining
Hong et al. (2018) studies the applicability and effectiveness of data mining technique in association to Fuzzy WolfE techniques towards predicting natural disasters such as flood and hurricanes.
The study establishes that the hybrid model called fuzzy woflE-SVM model is an effective model that helps to predict the future trend effectively.
This study is relevant for the topic of data mining because this study provides new method in relation to data mining in the aspect of climate control.
The uniqueness of this study is that it helps to understand how data mining technique is used for future prediction in association with Fuzzy WolfE technique.
The advantages of this research are that it helps to develop a unique hybrid model as well as their implementation process. However, the major disadvantage of this study is that the study itself does not provide any conclusive remarks on the effectiveness of this model due to the presence of uncontrollable factors.
Uncontrollable factors may affect the effectiveness of an appropriate data-mining algorithm in a industry context.
References
Dey, M., & Rautaray, S. S. (2014). Study and analysis of data mining algorithms for healthcare decision support system. planning, 5(6), 470-477
Gonçalves, J., Faria, B. M., Reis, L. P., Carvalho, V., & Rocha, Á. (2015, June). Data mining and electronic devices applied to quality of life related to health data. In Information Systems and Technologies (CISTI), 2015 10th Iberian Conference (pp. 277-280). IEEE.
Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A. X., & Chen, W. (2018). Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Science of the Total Environment, 625, 575-588.
Linden, A., & Yarnold, P. R. (2016). Using data mining techniques to characterize participation in observational studies. Journal of evaluation in clinical practice, 22(6), 839-847.
Papalexakis, E. E., Faloutsos, C., & Sidiropoulos, N. D. (2017). Tensors for data mining and data fusion: Models, applications, and scalable algorithms. ACM Transactions on Intelligent Systems and Technology (TIST), 8(2), 16-60.
Zhou, H., Qiao, L., Jiang, Y., Sun, H., & Chen, Q. (2016). Recognition of air-conditioner operation from indoor air temperature and relative humidity by a data mining approach. Energy and Buildings, 111, 233-241.
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