Fuzzy Mean Point Clustering using K-means algorithm for implementing the movecentroid function code
Journal Title: INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY - Year 2013, Vol 4, Issue 1
Abstract
The paper focus on combination of K-Means algorithm for Fuzzy Mean Point Clustering Neural Network (FMPCNN). The algorithm is implemented in JAVA program code for implementing the movecentroid function code into FMPCNN. Here we have provided movecentroid’s output to Fuzzy clustering as criteria, movecentroid is the base function of K-means algorithm as in Fuzzy Mean Point Clustering Neural Network (FMPCNN) algorithm, calculation of cluster based on pre-defined criteria and scope is done. In the experiment we have used four datasets and observed results in nano seconds there is huge difference in output as time is reduced for Fuzzy Min-Max code execution of fuzzy calculations of clustering.
Authors and Affiliations
Ganeshchandra Narharrao Shinde, Inamdar S. A. , Narangale S. M.
Keywords
Related Articles
- EP ID EP649887
- DOI 10.24297/ijct.v4i1b.3059
- Views 119
- Downloads 0
Finding a Relative Testing approach for Service Oriented Architecture
A SOA (Service Oriented Architecture) is an enterprise-scale IT architecture for linking resources on demand. In a SOA, resources are made available to participants in a value net, enterprise, and line of business. Servi...
An Efficient Intelligent System for Data Communication Using LIFI Technology
The congestion of radio spectrum has led to the development of new wireless technology, Light Fidelity also referred as LIFI, which has a much broader spectrum. LIFI is a technology based on visible light communication (...
A Group Collaboratable Proof of Retrievability Scheme for Cloud Data Storage
Cloud computing and cloud data storage have become important applications on the Internet. An important trend in cloud computing and cloud data storage is group collaboration since it is a great inducement for an entity...
A Model for Improving Classifier Accuracy using Outlier Analysis
Anomalies are those records, which have different behavior and do not comply with the remaining records in the dataset. Outlier analysis is the concept to find anomalies in Datasets. Â Detecting outliers efficiently is a...
A Novel LAS-Relief Feature Selection Algorithm for Enhancing Classification Accuracy in Data mining
Feature selection is an important task in data mining and machine learning domain. The main objective of feature selection is to find a relevant feature that predicts the knowledge better than the original set of feature...