The foremost determination of this unit of the paper is to focus on the limitations of data mining, the effectiveness of data mining and the misuse of data mining. The paper includes six scholarly journals; each journal is described on the basis of its strength, weakness and its relevance to the topic. The paper also focuses on the effectiveness of the data mining approaches.
Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176.
Recent studies indicated that lots of emerging methodologies used in the data mining process. The writer of this journal highlighted the different kinds of data sets considering data mining such as the packet level data and net flow data and public set data. The main strength of this document is that it guides the readers to know about all the potential risks associated with data mining but the one the limitations of this journal is that it does not discusses the issue related to the emerging methodologies which may have an impact of the overall process. This resource gives in-depth knowledge about artificial neutral networks, fuzzy association rules, clustering and Bayesian networks.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.
A recent study conducted on data mining stated the problems related to data mining such as the networking problems, data storage problems and data collection challenges. The journal proposes a big data processing model from the perspective of data mining. The most unique characteristics of this journal is Hace theorem which are applicable for organisation which deals with huge amount of big data. The prime strength of this article is that it gives an idea about the data mining challenges with big data and prime limitations of the article is its inability to minimize the issues in spite of knowing the challenges. The research provided an in-depth analysis of the data mining algorithms.
Correa, G., Assuncao, P. A., Agostini, L. V., & da Silva Cruz, L. A. (2015). Fast HEVC encoding decisions using data mining. IEEE transactions on circuits and systems for video technology, 25(4), 660-673.
Based on the study related to the encoding decisions of using data mining different schemes are proposed based on decision trees obtained through data mining techniques. The paper also helps in understanding the impacts of the techniques for which different complexity are significantly reduced in any organisation. One of the most intriguing features of this paper is the data related to the new flexible partitioning structures such as the prediction units, residual code trees and coding trees. The strength of this article is that it gives in depth knowledge about the timeframe of the partition structure algorithm and the weakness of this article is that the issues of the partition structure are not entirely discussed in the paper. The author stated that the data mining techniques are used by organisations for forecasting their sales and improve efficiency between the workers.
Tsai, C. W., Lai, C. F., Chiang, M. C., & Yang, L. T. (2014). Data mining for Internet of Things: A survey. IEEE Communications Surveys and Tutorials, 16(1), 77-97.
A model had been prepared by Tsai et al. (2014), focussed on the limitations of data mining in IoT. The paper highlights the role and responsibilities of data mining in IOT. The papers discuss the potential of data mining, limitations of data mining and all the future trends of data mining in IoT. The unique characteristic features of this paper are the detailed data mining process associated with the internet of things. The foremost strength of this paper is that it is very much useful to understand the ideas of application of data mining in IoT using different kinds of algorithms. The drawback of this article is the complex comparison chart between the various categories of data mining algorithms. Compared with the other similar articles this paper gives an in-depth knowledge about the various categories of data mining algorithm and the different combinations of data mining in IoT. The writer of this document highlighted the importance of data mining technique in identification of the latest emerging trend in order to maintain their quality of services. This resource gives in-depth knowledge about the application of data mining in IoT.
Huang, X. L., Wu, J., Li, W., Zhang, Z., Zhu, F., & Wu, M. (2016). Historical spectrum sensing data mining for cognitive radio enabled vehicular ad-hoc networks. IEEE Transactions on Dependable and Secure Computing, 13(1), 59-70.
Recent studies indicated the applications and limitation of data mining in the automobiles for the purposes of enhancing security of the passengers. The strength of the paper is that it states all the limitations associated with the technologies and the effectiveness of data mining approaches. Maintaining accounts and inventory is easier with the help of the data mining techniques as a suggested by this writer. The weakness of the paper is that it fails to provide remedial measures against those problems.
The paper discusses the different types of spatio temporal correlations among the historical spectrum sensing data which are essential for gathering knowledge about the risks involved in the radio enabled vehicles (Shmueli et al., 2017). The most unique characteristics of this journal are the statistical historical spectrum sensing data mining. The prime strength of this paper is that it significantly helps in improving the application level performance. The foremost drawback of this paper is that it failed to support any urgent safety applications. Compared to the other similar paper this paper mainly focuses the problems which are solved in an urgent basis. According to the author all the important business decision can be taken in a matter of second with the help of the data mining techniques. This resource gives in-depth knowledge about the sensors used in data mining considering the risks associated to the radio enabled vehicles.
Saranya, K., Premalatha, K., & Rajasekar, S. S. (2015, February). A survey on privacy preserving data mining. In Electronics and Communication Systems (ICECS), 2015 2nd International Conference on (pp. 1740-1744). IEEE.
The data of this study is a significant pointer towards the rising use of the data mining technologies led to issues such as privacy which is the main finding of this paper. The privacy preserving is the most topic of discussion of this journal. This paper helps in understanding the classification of the different techniques of the of privacy preservation data mining. The prime strength of this article is that it highlights the importance of the sensitive data of bigger organisation and the main drawback of this paper is that it do not discusses the issues related with the implementation of the PPDM techniques. Compared to the other similar papers this paper discusses the classifications of the PPDM techniques in a more detailed way so that the readers of this document can apply those techniques in their organisations. The paper only points out the problematic areas about the miss use of data mining.
The extensive use of the data mining approaches are seen in different kinds of industries such as healthcare industry and educational industry where it is used to collect and manage data efficiently for accomplishing the business objectives of their organization. Many issue and challenges are solved with the use of the data mining techniques.
Considering the limitations of data mining in IOT in data mining, one of the most important technique by which this issue can be solved is the tracking patterns. The use of this technique can significantly have an impact regarding the minimization of the limitations of data mining (Buczak & Guven, 2016). Using this technique, the problem related to data mining in IOT is partially solved as there are other associated issues related to IoT which cannot be purposefully solved. The application of this technique can have a positive impact on the different types of IOT based systems
The inappropriate use of the data mining technologies has a direct negative impact on the organisation which incorporates this technology. Any kind of misuse or alteration of data is harmful for the growth and reputation of the organisation. the misuse of data mining should prevented with help of strict cyber security laws. Sometime due to lack of experienced this technology is miss-utilized. The limitation of data mining should be properly understood before applying the mentioned risk mitigation technique (Chen et al., 2015). Misuse of data mining is the other type of drawback of data mining. The other technique by which this problem can be solved is the proper knowledge and awareness of the most common issues related to the IoT. All the probable risks and vulnerabilities associated with data mining in IoT should be predefined to the users of the IoT enabled systems.
Conclusion
From the above literature search, it can be concluded that there are plenty of issues related to the application of data mining across different organisation. This paper helps in understanding the limitations of data mining in its different applications. The paper highlighted the importance of preventing the misuse of data mining which can have a direct negative impact on any organisation. So, it can be acknowledged that there are different techniques by which the limitations can be minimized extent. The paper concludes with any alternative technique by which the limitations of data mining can be overcome.
References
Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176.
Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A. V., & Rong, X. (2015). Data mining for the internet of things: literature review and challenges. International Journal of Distributed Sensor Networks, 11(8), 431047.
Correa, G., Assuncao, P. A., Agostini, L. V., & da Silva Cruz, L. A. (2015). Fast HEVC encoding decisions using data mining. IEEE transactions on circuits and systems for video technology, 25(4), 660-673.
Huang, X. L., Wu, J., Li, W., Zhang, Z., Zhu, F., & Wu, M. (2016). Historical spectrum sensing data mining for cognitive radio enabled vehicular ad-hoc networks. IEEE Transactions on Dependable and Secure Computing, 13(1), 59-70.
Saranya, K., Premalatha, K., & Rajasekar, S. S. (2015, February). A survey on privacy preserving data mining. In Electronics and Communication Systems (ICECS), 2015 2nd International Conference on (pp. 1740-1744). IEEE.
Shmueli, G., Bruce, P. C., Patel, N. R., Yahav, I., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons.
Tsai, C. W., Lai, C. F., Chiang, M. C., & Yang, L. T. (2014). Data mining for Internet of Things: A survey. IEEE Communications Surveys and Tutorials, 16(1), 77-97.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.
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