Clustering Based Algorithm for Efficient and Effective Feature Selection Performance
Journal Title: International Journal of Innovative Research in Computer Science and Technology - Year 2014, Vol 2, Issue 3
Abstract
Process with high dimensional data is enormous issue in data mining and machine learning applications. Feature selection is the mode of recognize the good number of features that produce well-suited outcome as the unique entire set of features. Feature selection process constructs a pathway to reduce the dimensionality and time complexity and also improve the accuracy level of classifier. In this paper, we use an alternative approach, called affinity propagation algorithm for effective and efficient feature selection and clustering process. The endeavor is to improve the performance in terms accuracy and time complexity.
Authors and Affiliations
K. Revathi, T. Kalai Selvi
Keywords
Related Articles
- EP ID EP749193
- DOI -
- Views 41
- Downloads 0
A Review on Bio-alcohol as Green Energy
Bioethanol has grown into a large business, and it seems that it will continue to grow in the foreseeable future. Although certain challenges, such as rivalry among bioethanol and human food, bioethanol is widely recogni...
Finding Hit And Run Vehicle Using GPS Tracker
An efficient vehicle tracking system is designed and implemented for tracking the movement of any equipment vehicle from any location at any time. The proposed system made good use of a popular technology that combines a...
An Overview on the Study of Data Encryption and Decryption in Cloud Computing
While big data technologies in cloud computing are rapidly growing in popularity, privacy issues have increased dramatically. Encrypting data in real-time is one of the greatest important issues throughout data acquisiti...
Survey on Challenges in DevOps
DevOps could be a culture, movement or observe that emphasizes the collaboration and communication of each computer code developers and different information-technology(IT) rofessionals whereas automating the method of c...
A Stacked Ensemble Framework for Detecting Malicious Insiders
One of the mainstream strategies identified for detecting Malicious Insider Threat (MIT) is building stacking ensemble Machine Learning (ML) models to reveal malevolent insider activities through anomalies in user activi...