IMPROVING THE SOFTWARE ARCHITECTURE THROUGH FUZZY CLUSTERING TECHNIQUE.
Journal Title: Indian Journal of Computer Science and Engineering - Year 2010, Vol 1, Issue 1
Abstract
Software Architecture Recovery is one of the finest parts of reverse engineering. Several different techniques have been adopted in vast literature to recover Software Architecture. One of the techniques is clustering, which extracts the similar components from the software. In general the component characteristics and its state are not clear. For that, Architecture recovered from ordinary clustering will not be appropriate in it. In this paper we used a fuzzy clustering technique to make the Software Architecture recovery to be more efficient and accurate. Our experimental results have shown that Architecture recovers from fuzzy clustering is better than ordinary clustering
Authors and Affiliations
Shaheda Akthar , Sk. Md. Rafi
ANALYSIS ON PERFORMANCE WITH COMBINATION OF SCHEDULING AND REPLICATION IN DATA GRIDS
Data grid is the storage component of a grid environment. Amount of data transferred among nodes can be reduced by submitting the jobs to the nodes that having maximum requested files by scheduling and reducing the acces...
The Anatomy of Web Search Result Clustering and Search Engines
World Wide Web is a very large distributed digital information space. The ability to search and retrieve information from the Web efficiently and effectively is an enabling technology for realizing its full potential. Cu...
Image Processing Techniques and Neural Networks for Automated Cancer Analysis from Breast Thermographs-A Review
Clinical Infrared Thermography is the best suited technique for early detection of breast cancer. Interpretation of breast thermographs helps in identifying the abnormality in the region. In recent years, computer Aided...
A GLFES and DFT Technique for Feature Selection in High-Dimensional Imbalanced dataset
Feature selection has been an active research area in pattern recognition, statistics ,and data mining communities Feature selection, is a preprocessing step to machine learning, is effective in reducing dimensionality,...
Comparison and Analysis of RREQ and RREP for Dynamic Wireless Network
This paper compares and analyzes RREQ and RREP for wireless networks using Zigbee technology for transferring data between dynamically moving nodes. The system is simulated on Ptolemy 2 Visual Sense simulating tool, usin...