Computer Science Engineering

Hierarchical agglomerative clustering merges the clusters basing on their distance similarity. In this paper
we present a new mathematical method called D metric spaces by Indian mathematician B.C.Dhage who
has submitted his thesis in 1984 at Maratwada university. We present an algorithm for hierarchical
clustering using D metric concept instead of Euclidean distance which reduces the total number of
iterations ,complexity and computation time. Here we showed an example that contains the results of both
techniques and also comparisons.

Breast cancer is considered as common expiry factor for women in India. If it is identified earlier better treatment can be given and survivability rate can be improved. Sometimes symptom look like breast cancer but it is not malignant. To handle this type of condition situations we have to examine the breast tissue. A lot of research is going on in this area from the past few years. Machine learning is fast growing field in computer science which provides better prediction methodologies for diseases in health care management, hence it was applied in the area of the breast cancer and lot of results produced by several researchers. Ensemble learning is nothing but group of classifiers which in reality yielding better results rather than the existing results. To produce the better results we use collection of classifiers called ensembles. In this research we have implemented ensemble methods to improve the better prediction for breast cancer to classify the breast tissue as in the form of carcinoma and fibroadinoma .Along with existing classifiers like J48Naive Bayes, random forest and SMO. We implemented ensemble classifiers like Adaboosting, bagging and stacking or blending methods with them, in reality it is showing better accuracies [1].

Anxiety disorders are most rapidly increasing in the present society. Among them one of the serious disorders is GAD stands for Generalized Anxiety Disorder which is observed in various cases of people. We have implemented Artificial Neural Networks using sensitivity analysis and without using sensitivity analysis for developing of better predictive models for GAD. The results were observed that sensitivity analysis improves the performance of neural networks when compared with without using sensitivity analysis.