A hybrid model of hierarchical clustering and decision tree for rule-based classification of diabetic patients.

Hybrid models in data mining have recently gained attention including in the study of medical research. Various studies in this domain using hybrid models have shown different results. This paper presents the new hybrid model by exploring Agglomerative Hierarchical Clustering and Decision Tree Classifier on Pima Indians Diabetes dataset. The experiments compared performance accuracy of the Decision Tree Classifier against the same classifier augmented with Hierarchical Clustering. Results showed that the hybrid model achieved higher accuracy with 80.8% as compared to 76.9% of the standard model. This is a promising result for adoption of hierarchical clustering in a rule-based classifier

Measurement of software maintenance from user satisfaction perspective: a case study

The importance of software maintenance in a software development environment is a long term and continuous activity for improvement. The service quality of the software maintenance provided in which involving helpdesk, programmer and software analyst should be measured in order to satisfy users who use the software. In this paper, we propose a software measurement program for product improvement in order to maximize the user satisfaction where one government agency is selected as our case study. Our measurement plan is based on Goal Question Metric (GQM) approach. Results from this measurement program with some meaningful quantitative analysis has given an achievable roadmap for the organization to plan for next improvement activities toward their goals