An Enhanced k-means algorithm to improve the Efficiency Using Normal Distribution Data Points
Journal Title: International Journal on Computer Science and Engineering - Year 2010, Vol 2, Issue 7
Abstract
Clustering is one of the unsupervised learning method in which a set of essentials is separated into uniform groups. The k-means method is one of the most widely used clustering techniques for various applications. This paper proposes a method for making the K-means algorithm more effective and efficient; so as to get better clustering with reduced complexity. In this research, the most representative algorithms K-Means and the Enhanced K-means were examined and analyzed based on their basic approach. The best algorithm was found out based on their performance using Normal Distribution data points. The accuracy of the algorithm was investigated during different execution of the program on the input data points. The elapsed time taken by proposed enhanced k-means is less than k-means algorithm.
Authors and Affiliations
D. Napoleon , P. Ganga Lakshmi
Keywords
Related Articles
- EP ID EP144998
- DOI -
- Views 155
- Downloads 0
HIDDEN WEB EXTRACTOR DYNAMIC WAY TO UNCOVER THE DEEP WEB
In this era of digital tsunami of information on the web, everyone is completely dependent on the WWW for information retrieval. This has posed a challenging problem in extracting relevant data. Traditional web crawlers...
Architectural Based Data Aggregation Techniques in Wireless Sensor Network: A Comparative Study
Data aggregation is very crucial techniques in wireless sensor network. Because with the help of data aggregation we reduce the energy consumption by eliminating redundancy .when wireless sensor network deployed in remot...
Improving Throughput in Cloud Storage System
Because the cloud serves many workloads concurrently, its disk access pattern is highly random and heterogeneous. In addition, because various virtual machines access to files respectively, meta data utilization and smal...
A Novel Texture Synthesis Algorithm Using Patch Matching by Fuzzy Texture Unit
Texture is an important spatial feature useful for identifying objects or regions of interest in an image. This paper presents a novel texture characterization method based on Fuzzy Texture Unit (FTU) for texture synthes...
Quantum Computation and Consciousness in Cyclic and Mythological Models of Universe
Cyclic models such as Steinhardt-Turok model, Baum-Frampton model, and CCC models have been proposed for the universe. It has been postulated that the value of the physical constants in different aeons may possibly be di...