International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-5, Issue-4, April 2016 200 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. Abstract— Data mining, the concept of unseen predictive information from big databases is a powerful novel technology with great potential used in various commercial uses including banking, retail industry, e-commerce, telecommunication industry, DNA analysis remote sensing, bioinformatics etc. Education is a required element for the progress of nation. Mining in educational environment is called Educational Data Mining. Educational data mining is concerned with developing new methods to discover knowledge from educational database. In order to analyze opinion of students about their teachers in Professor Appraisal System, this paper surveys an application of data mining in Professor Appraisal System & also present result analysis using CLEMENTINE 12.0 tool. There are varieties of popular data mining task within the educational data mining e.g. classification, clustering, outlier detection, association rule, prediction etc. How each of data mining tasks can be applied to education system is explained. In this paper we analyze the performance of final Faculty Appraisal of a semester of a computer engineering department, Vignan Institute of Information Technology College of engineering & is presented the result which it is achieved using CLEMENTINE 12.0 tool. We have verified hidden patterns of Faculty Appraisal by students and is predicted that which Faculty will be invited to faculty classes and which Faculty will be refusing and department heads due to Appraisal reasons will ask explanations with them. Index Terms— Classification, Clustering, Association rule, Data mining, Appraisal, CLEMENTINE 12.0. I. INTRODUCTION Data mining has involved a great deal of responsiveness in the information industry and in society as a whole in recent years, due to the wide availability of huge amounts of data and the forthcoming need for turning such data into useful information and knowledge. The information and knowledge gained can be used for applications ranging from market analysis, fraud detection, and customer retention, to production control and science exploration [1]. Manual data analysis has been around for some time now, but it creates a bottleneck for large data analysis. The transition won't occur automatically; in this case, there is a need for data mining [2]. Mining applied in education was published in 1995 by Sanjeev and Zytkow. Researchers gathered the knowledge discovery as terms like “P pattern for data in the range R” Revised Version Manuscript Received on April 27, 2016. Ramakrishna Gandi, CSE Department,Vignan’s Institute of Information Technology, Visakhapatnam(A.P),India. Prathimarani Palla, CSE Department, Vignan’s Institute of Information Technology, Visakhapatnam (A.P),India. Madhuri Thimmapuram, CSE Department, Vignan’s Institute of Information Technology, Visakhapatnam (A.P), India. Daniel Prasanth T,CSE Department, Vignan’s Institute of Information Technology, Visakhapatnam (A.P),India. from university database [3]. Vranić and Skoćır was examined how to improve some aspects of educational quality with data mining algorithms and techniques by taking a specific course students as target audience in academic environments [4].In this paper we have collected information and results of a appraisal about 30 professors in Vignan Institute of Information Technology College of Engineering, Department of Computer Engineering on professor's performances in classroom then with data mining algorithms such Association Rule and decision trees (C&RT) , it is proceeded to analyze and predict acceptation of a professor for continuing the teaching in that subject .There are new rules and relations between selected parameters such as Teaching, Professor Degree, Preparation, Communication, Class Control, Teaching experience, Approved Staff to next semesters on professor appraisal system that is interested for Heads of Departments of Institution. II. METHODOLOGY In this research study, We have followed a popular data mining methodology called Cross Industry Standard Process for Data Mining (CRISP-DM), which is a six-step process [5]: • Problem explanation: Comprises understanding development goals with business perspective. • Understanding the data: Includes identifying the sources of data. • Formulating the data: Includes pre-processing, cleaning, and transforming the relevant data into a form that can be used by data mining algorithms. • Creating the models: Includes developing a wide range of models using comparable analytical techniques. • Assessing the models: Includes evaluating and assessing the validity and the utility of the models against each other and against the goals of the study. • Using the model: Includes in such activities as deploying the models for use in decision making processes. Fig.1.A graphical illustration of the methodology employed in this study A Novel Approach for Faculty Appraisal in Educational Data Mining using CLEMENTINE TOOL Ramakrishna Gandi, Prathimarani Palla, Madhuri Thimmapuram, Daniel Prasanth T