Data mining consists of extracting implicitly potentially valuable and unknown information from the sets of data (Witten et al., 2016). The exploration and analysis of large amount of data by means of technology or devices for discovering some meaningful patterns in them can be termed as Data Mining too.
The phases of data mining process includes business understanding, data preparation, data understanding, data modeling, model evaluation, and deployment (Larose, 2014). All these stages consist of some steps that had helped in the formation of the data mining model and implementing it for the data operations of any business organization.
The various stages or phases of data mining process are evaluated in terms of their actions and plan. The explanation of the stages of data mining process are provided below,
Business Understanding: The business understanding is the phase where the data miner forms a clear understanding of the requirements of the organization with the data mining process (Wu et al., 2014). The steps included in this phase are identification of the business goals, defining of the data mining goals, and forming the project plan. It includes the formation of the prior knowledge before starting the data mining process.
Data Understanding: The data understanding is the process of forming an analysis of the available data and reviewing them for the data mining process (Rokach & Maimon, 2014). This phase consists of the process of gathering data, describing them, exploring the data management, and verifying the quality of the available data. The main activities of the data understanding process is the identification of the data management and reviewing the issues of data quality.
Data Preparation: According to Freitas (2013), it is the most hectic phase of the data mining process. In data preparation phase, the accumulated data would be stacked for initializing the modeling in the data mining process. The processes included in the phase are data selection, data construction, data cleaning, formatting the data, and integration of data. The data preparation had been helpful for making the data ready for the data mining and modeling process. The data preparation would be formed for carrying out the data set sorting that could be used in the later stages.
Data Modeling: The data modeling is the phase in which the data that has been filtered, sorted and processed would be analyzed for forming a pattern of similarity in the data set (Baker & Inventado, 2014). The data modeling had deployed the use of some mathematical techniques and processes for the identification of the data. The data modeling phase includes the processes of selecting appropriate technique for data modeling, designing tests for the data model, building models, and assessing the models. The data models are made by selecting any one of the data mining functions (classification, summarization, association, regression, and clustering) deployment (Larose, 2014). The selection of data mining models had been helpful for critically evaluating the data with a specific algorithm for the data reduction and transformation.
Model Evaluation: The model evaluation is the phase in which the data model of the data mining is evaluated for making it ready for the deployment (Rokach & Maimon, 2014). The evaluation process consists of the processes of result evaluation, review process, and determination of the next step. In this process, the model that had been made in the prior phase would be reviewed for finding out whether the model formed is compatible with the business operations or not. The patterns formed in the data modeling phase would be presented and evaluated by following the steps of visualization, transformation, and removal of redundant pattern. The final model after evaluation is made ready to be deployed in the operations of the business organizations.
Deployment: It is the final step of data mining process that involves the implementation of the data model in the operations of the operations of the organizations (Freitas, 2013). The deployment phase consists of making plan for deployment, reporting the final results of the implemented model, and reviewing the results of the final implementation. The deployment of the data model would be helpful for using the data mining model for the operations of the business organizations. The use of the discovered knowledge had been helpful for deploying the model in usability functions.
The major decisions that the data miner had to take while designing the data mining process are pointed out below,
The data mining has linear stages of functions that include the phases of business understanding, data preparation, data understanding, data modeling, model evaluation, and deployment (Wu et al., 2014). The operations of these phases are dependent on the prior stages. For example, unless the data is selected in the data understanding phase, the process of data modeling cannot be completed. The operations of the data mining is iterative in nature as the final step of the data mining returns back to the first step of data integration. The following figure would show the iterative nature that is witnessed in the operations of data mining:
Figure 1: Iterative nature of Data Mining Process
(Source: Demsar et al., 2013, pp-2352)
The data mining process is useful for managing and extracting useful data and information from the large data sets present (Lu, Setiono & Liu, 2017). The applications of the data mining are not limited to business processes and business organizations. The data mining finds its use for education, security, and bio researches.
Data mining in Education: The educational data mining is a new field that deploys the discovering of the knowledge (Fan & Bifet, 2013). The data mining would help in assisting the process of prediction of learning behavior and educational support. The study had been helped by the use of data mining processes. The goal of the data mining for the purpose of education is to form the process of accurate decision making process in the organization. The institution made with the help of data mining process is helpful for focusing on the teaching prospect for the students. The data mining can be used for capturing the learning patterns and developing appropriate techniques for teaching them.
Data mining in Security: The data mining had been helpful for forming defensive measures to avoid programming errors and form information protection (Lin et al., 2013). The data mining had been helpful for supporting the user authentication, fixing programming errors and focusing on the anomaly detection. Any activity in the system can be distinguished with the help of data mining methods. It can be used in preparing the model of lie detection. The data mining had been helpful for law enforcement too.
Data mining in Bio researches: The data mining is helpful for supporting the research activities of Bioinformatics (Lu, Setiono & Liu, 2017). The use of mining of biological data had been helpful for extracting crucial information and assisting the medicine and neuroscience. For example: The data mining had been used for researching on the gene findings. Some other applications include diseases treatment, data cleansing, disease prognosis, and sub cellular location prediction.
References
Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In Learning analytics (pp. 61-75). Springer New York.
Demšar, J., Curk, T., Erjavec, A., Gorup, ?., Ho?evar, T., Milutinovi?, M., … & Štajdohar, M. (2013). Orange: data mining toolbox in Python. Journal of Machine Learning Research, 14(1), 2349-2353.
Fan, W., & Bifet, A. (2013). Mining big data: current status, and forecast to the future. ACM sIGKDD Explorations Newsletter, 14(2), 1-5.
Freitas, A. A. (2013). Data mining and knowledge discovery with evolutionary algorithms. Springer Science & Business Media.
Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons.
Lin, T. Y., Yao, Y. Y., & Zadeh, L. A. (Eds.). (2013). Data mining, rough sets and granular computing (Vol. 95). Physica.
Lu, H., Setiono, R., & Liu, H. (2017). Neurorule: A connectionist approach to data mining. arXiv preprint arXiv:1701.01358.
Rokach, L., & Maimon, O. (2014). Data mining with decision trees: theory and applications. World scientific.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
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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