please post the source code for clustering with multiviewpoint based similarity measure iee paper ....etc

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source code of mobile sink based energy efficient clustering algorithm in matlab

Abstract

Energy hole problem is a critical issue for data gathering in wireless sensor networks. Sensors near the static sink act as relays for far sensors and thus will deplete their energy very quickly, resulting energy holes in the sensor field. Exploiting the mobility of a sink has been widely accepted as an efficient way to alleviate this problem. However, determining an optimal moving trajectory for a mobile sink is a non-deterministic polynomial-time hard ....etc

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Demo.m shows a K-means clustering demo
kmeans_function folder contains following files to show how it works as a function:
Test.m
km_fun.m

K-means clustering is one of the popular algorithms in clustering and segmentation. K-means clustering treats each feature point as having a location in space. The basic K-means algorithm then arbitrarily locates, that number of cluster centers in multidimensional measurement space. Each point is then assigned to the cluster whose arbitrary mean vector is closest. The procedure continues until th ....etc

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/**
* %SVN.HEADER%
*/
package net.sf.javaml.clustering;

import java.util.Random;

import net.sf.javaml.core.Dataset;
import net.sf.javaml.core.DefaultDataset;
import net.sf.javaml.core.Instance;
import net.sf.javaml.distance.DistanceMeasure;
import net.sf.javaml.distance.EuclideanDistance;
import net.sf.javaml.tools.DatasetTools;

/**
* Implementation of the K-medoids algorithm. K-medoids is a clustering
* algorithm that is very much like k-means. The main difference between the two
* algorithms is the cluster center they use. K-means use ....etc

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k-mediods clustering algorithm source code in java. ....etc

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To get full information or details of hierarchical clustering please have a look on the pages

http://seminarsprojects.net/Thread-efficient-clustering-scheme-to-exploit-hierarchical-data-in-network-traffic-analysis

http://seminarsprojects.net/Thread-ontology-discovery-for-the-semantic-web-using-hierarchical-clustering

if you again feel trouble on hierarchical clustering please reply in that page and ask specific fields in hierarchical clustering ....etc

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To get full information or details of fuzzy c means clustering please have a look on the pages

http://seminarsprojects.net/Thread-fuzzy-c-means-clustering-of-incomplete-data

http://seminarsprojects.net/Thread-fuzzy-c-means-clustering-based-digital-camouflage-pattern-design

if you again feel trouble on fuzzy c means clustering please reply in that page and ask specific fields in fuzzy c means clustering ....etc

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/**
* %SVN.HEADER%
*/
package net.sf.javaml.clustering;

import java.util.Random;

import net.sf.javaml.core.Dataset;
import net.sf.javaml.core.DefaultDataset;
import net.sf.javaml.core.Instance;
import net.sf.javaml.distance.DistanceMeasure;
import net.sf.javaml.distance.EuclideanDistance;
import net.sf.javaml.tools.DatasetTools;

/**
* Implementation of the K-medoids algorithm. K-medoids is a clustering
* algorithm that is very much like k-means. The main difference between the two
* algorithms is the cluster center they use. K-means use ....etc

[:=Read Full Message Here=:]

quality threshold clustering algorithm implementation source code

Abstract

Quality Threshold Clustering (QTC) is an algorithm for partitioning data, in fields such as biology, where clustering of large data-sets can aid scientific discovery. Unlike other clustering algorithms, QTC does not require knowing the number of clusters a priori, however, its perceived need for high computing power often makes it an unattractive choice. This paper presents a thorough study of QTC. We analyze the worst case complexity of the algorithm and discuss metho ....etc

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k medoids clustering algorithm java source code

ABSTRACT:

Clustering is the process of classifying objects into different groups by partitioning sets of data into a series of subsets called clusters. Clustering has taken its roots from algorithms like k-medoids and k-medoids. However conventional k-medoids clustering algorithm suffers from many limitations. Firstly, it needs to have prior knowledge about the number of cluster parameter k. Secondly, it also initially needs to make random selection of k representative objects and if these ini ....etc

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