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Data Mining at University

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            Data mining defines the latest technology of discovery of the knowledge that has not yet revealed before from the large sets of data. The knowledge gets hidden such that the acquisition guarantees the best benefits to the institution. The university takes to have many research materials, and it require the application of special techniques in the analyzing of the data; otherwise, the useful hidden knowledge cannot get revealed. Therefore, the data mining is the process of applying some defined techniques to analyze the large sets of data stored I the databases or the data warehouse for the acquisition of the hidden useful information (knowledge) that is beneficial to the owner of the data. As the comparison of the data from the different perspectives gets done, the other aspect of having the process is to get the accomplishment of tasks and goals at the relative apprenticing cost. The process will get to have the provision of the information that results in a better understanding of the concepts and gets to guarantee the growth of critical thinking that will automatically get to making sound decisions that get to see the success and accomplishment of the tasks. Furthermore, primarily the data mining gets to assist the making the relevant observation and the finding of the correlations amongst the database fields and the relationships between the different facts of the same database (Principles of data mining., 2000).

            Data mining gets characterized with five major elements. The process gets to have the extraction of the data transform the data into relative information and get to store the relation within the data warehouse system. The data gets stored and managed in multidimensional database system means the all correlations gets incorporated into the system. the process gets to provide the sensitive and the hidden plus the ordinary data to the business analyst and the information technology professionals. Business analyst gets to have the data for the enhancement and purpose of the business intelligence. Data gets analyzed by the application of different application software that guarantees quality. The information gets presented into useful formats such as the graph or tables if I may mention.

Data mining advantages for the University

            Data mining in the context of education at times get referred to as the knowledge discovery in databases. The application of the process within the university will get the boost to the students in the undertaking of the research. The extraction of relevant patterns and trends of some data within the database relating to experimentally oriented researchers will get to the discovery of the hidden mechanism. It is appreciatible to the fact that university database contains huge information within the database as many reports have got presented, and the only use of the data is the undertaking of data mining to have the realization of knowledge.

            According to Romdhae et al. (2010), data mining is useful for the predictions of the students profiling. The patterns and the trends of the students operations and relations can get to the draw of the student model that will get to describe the particular habits of the students, the needs and the behavior of a particular or student or a group of the student within the schools. This will get the management to handle easily and output the most reliable and products graduates of their uniqueness. The data mining gets the further definition of the student demographic, geographic, and the psychographic features of the respective students.

            Furthermore, the data mining process will get the university to stand the chance of developing the curriculum. The data mining process will get through the study of the course preferences by the students, the completion rates for the particular courses and the enrollees profession. The correlation established within the course category, and the profession enrollee gets to have the determination of the preferable course thus the development of the education curriculum (Hsia et al., 2008).

            Data mining takes to rely on the four distinctive mechanisms of classification, categorization, estimation, and visualization. The university will take to have the each method simplifying a particular module of benefit to the institution. The undertaking of the data mining within the university will get identification of the association and clusters within the university subjects of study, and this defines the classification. The categorization may take to apply the rule of induction algorithm to define the drop outs of the students, transfers, and the willing to stay students while the estimation will get to provide predictive functions within the institution regarding the operations and the available resources. Visualization gets to provide the better look of the rules and presentation of the student score through the application of charts and graphs (Luan, 2004).

Data mining plan at the University

            According to Brown (2014), data mining takes five steps. The activities get done with the application of the appropriate data mining tools and techniques. Firstly, get to identify the source of information that you wish to conduct the data mining. Take to identify the datasets such as the information relating the score or student enrollment to determine the information to get studied for the retrieve of the pattern. Secondly, the process goes to the level where the identification of the particular data for the analysis gets done. The related data gets identified for the undertaking of the analysis and in most cases the Bayesian techniques take applied for the building of the corpus of the data. The third step of data mining process gets to the level of now extracting the relevant information from the identified data; this defines the start of the actual data analysis that gets to the sieving level of the database identified data. The fourth step of data mining gets to the identification of the key values from the respective data sets. Therefore, there is the extraction of the identified key values from the particular data. The key values are the information to get moved to the fifth stage. The last fifth stage is the interpretation and the presentation of the data mining results. The interpretation and reporting take to have the use of the special application software that takes to guarantee quality and reliability.

Data mining related issues and their mitigation

            Data mining gets affected with some issues that in all cases caution should get taken in the relation to the challenges. The mining technologies and user interaction issues involve the mining of the different unrelated kind of knowledge in the same database as users have different interests and priorities. The issues further include the interactive mining of the knowledge at multiple levels of abstractions, the handling of the incomplete data within a database, and the challenge with the presentation and visualization of the data mining results. Furthermore, the performance challenges include the scalability and the efficiency of the data mining algorithms and the distribution of the data and the existence of huge databases (Saranya, 2013). The mitigation for the issues should get the application of the appropriate data mining tools, knowledge-based systems, and the expert systems.

References

Brown, M. (2014). 5 steps to start Data Mining. Retrieved from http://scitechconnect.elsevier.com/5-steps-start-data-mining/

Hsia, Tai-Chang., Shie, An-Jin and Chen, Li-Chen., (2008) “Course Planning of extension education to meet market demand by using data mining technique: an example of Chinkuo Technical University in Taiwan.” Expert System with Applications, 34: 596-602

Luan, J. (2004). Data Mining Applications in Higher Education.

NATO Advanced Research Workshop on Improving Disaster Resilience and Mitigation – IT Means and Tools, & In Teodorescu, H.-N. (2014). Improving disaster resilience and mitigation — IT Means and Tools.

Principles of data mining. (2000). Cambridge, Mass: MIT Press.

Romdhae, L.B., Fadhel, N. and Ayeb, B. (2010) “An Efficient approach for building customer profiles from business data.” Expert System with Applications, 37: 1573-1585

Saranya, V. (2013). Major Issues in Data Mining. Retrieved from http://www.slideshare.net/ersaranya/major-issues-in-data-mining

Sherry Roberts is the author of this paper. A senior editor at MeldaResearch.Com in cheap term papers if you need a similar paper you can place your order from top research paper writing companies.

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