Software Effort Estimation with Data Mining Techniques- A Review

Effort Estimation is an important task in cost prediction of the software. This task comes under the planning phase of software project management. In this paper, a review of different data mining techniques used for effort estimation has been presented. The techniques taken into consideration are Clustered techniques (K-Means, K-NN-K-Nearest Neighbour), Regression techniques (MARS- Multivariate analysis for regression splines, OLS - Ordinary least square regression, SVR-Support vector regression, CART- classification and regression trees ) and classification techniques (SVM-Support vector machine, CBR-Case based reasoning). We can use the hybrid approach of these techniques for improving effort estimation.