AbstractsEngineering

The dissertation titled Effective Performance of Integrated K family newlineClusters and Decision Tree Structures on Quality Assessment of Institutional newlineData focuses primarily on the demand for data mining in educational newlineInstitutions which is constantly going up due to the development of newlineinformation systems in this age of globalization The quality of data in newlineeducational institutions has not only immense influence but also plays a key newlinerole in technology newlineBy analyzing the reasons of low data quality on System Engineering newlineTheory a new method called data mining has been established which solves newlinethe data quality problem by a meta synthesis method that includes software newlinedesigning management and data mining, testing etc Its application shows newlinethat it has good practicality which can increase the institution s decision newlinelevels However the main objective of educational institution is to impart newlinequality education One way to reach the highest level of quality in education newlinesystems is by improving the decision making processes such as assessment newlineevaluation counseling and so on Data mining absolutely needs high quality newlinedata because no sufficient method is available to get quality data in the newlineinstitutional zone and therefore data mining and fuzzy based approach can newlinebe applied to find relationships between the attributes of student and staff and newlineget a conclusion Of course the growth of educational and institutional newlinesystems depends upon the quality of services under critical circumstances newlineThe faculty profile student performance and infrastructure requirements are newlineinstitutional performance Upon the decision making procedures such as newlineplanning counseling assessment evaluation and conformation the highest newlinelevel of quality can be attained Above all this can be achieved and utilized newlineby means of managerial and succinct decisions based upon implicit newlineknowledge The knowledge that remains hidden in the educational data set newlineand it can be extracted from data mining technology newline newline%%%appendix p192-196, reference p197-204.