Students like to find better engineering college for their higher education. It is very challenging to find the better engineering college with conflicting criteria. In this research, the criterion such as academic reputation and achievements, infrastructure, fees structure, location, quality of the faculty, research facilities and other criterion are considered to find the better engineering college. Multi Criteria Decision Making (MCDM) is the most well known branch of decision making under the presence of conflicting criteria. TOPSIS is one of the MCDM technique widely applied to solve the problems which involves many number of criteria. In this research, TOPSIS is Adaptive and applied to find better engineering college. To evaluate the proposed methodology the parameters such as time complexity, space complexity, sensitivity analysis and rank reversal are considered. In this comparative analysis, better results are obtained for Adaptive TOPSIS compared to COPRAS.

Multi Criteria Decision Making (MCDM) methods are useful for evaluating several complex factors of multiple selection problems. The Multi-Objective problems are an extension of Single-Objective problems. The goal of MCDM is to help the decision maker to make a choice among a finite number of alternatives or to sort or rank a finite set of alternatives in terms of multiple criteria. Among the MCDM methods, the most widely applied method is TOPSIS. It is applied for different kinds of MCDM problems. In laptop selection process, it is difficult to select better laptop because relatively all laptops are seems to be same. By applying the TOPSIS method to the alternatives it is simple to differentiate the laptops from one another. The better laptop has been selected using TOPSIS based on conflicting criteria such as warranty, size, battery life, specification and others. This methodology also has been evaluated by MCDM evaluation metrics such as Time and Space Complexity, Sensitivity Analysis, ranking reversal and relative closeness coefficient. 

Qualitative bankruptcy prediction rules represent experts' problem-solving knowledge to predict qualitative bankruptcy. The objective of this research is predicting qualitative bankruptcy using ant-miner algorithm. Qualitative data are subjective and more difficult to measure. This approach uses qualitative risk factors which include fourteen internal risk factors and sixty eight external risk factors associated with it. By using these factors qualitative prediction rules are generated using ant-miner algorithm and the influence of these factors in bankruptcy is also analyzed. Ant-Miner algorithm is a application of ant colony optimization and data mining concepts. Qualitative rules generated by ant miner algorithm are validated using measure of agreement. These prediction rules yields better accuracy with lesser number of terms than previously applied qualitative bankruptcy prediction methodologies.

Decision Tree is the most widely applied supervised classification technique. The learning and classification steps of decision tree induction are simple and fast and it can be applied to any domain. In this research student qualitative data has been taken from educational data mining and the performance analysis of the decision tree algorithm ID3, C4.5 and CART are compared. The comparison result shows that the Gini Index of CART influence information Gain Ratio of ID3 and C4.5. The classification accuracy of CART is higher when compared to ID3 and C4.5. However the difference in classification accuracy between the decision tree algorithms is not considerably higher. The experimental results of decision tree indicate that student’s performance also influenced by qualitative factors.