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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k-mediods clustering algorithm source code in java. ....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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Abstract
All clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel multi-viewpoint based similarity measure and two related clustering methods. The major difference between a traditional dissimilarity/similarity measure and ours is that the former uses only a single viewpoint, which is the origin, while the latter utilizes many different viewpoints, which are objects ass ....etc

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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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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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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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java code for fuzzy c means clustering algorithm

Abstract

Data clustering is the process of dividing data elements from original images into classes or clusters so that items in the same class are as similar as possible, and items in different classes are as dissimilar as possible. Depending on the nature of the data and the purpose for which clustering is being used, different measures of similarity may be used to place items into classes, where the similarity measure controls how the clusters are formed. Some examples of measures that can b ....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=:]

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

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

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

[:=Read Full Message Here=:]