What Is 3-K-Means Clustering Algorithm

Figure 10 shows the results between the hybridg algorithm compared to other algorithms with various mathematical functions as presented by privous sessions. It is cleard to see that the hybride algorithm performed better than other techniques. Surrprisingly, the Ackley function performed closely to the hybride algorithm. Figure 11 - 14 shows that PSO reached the optimal result very quickly because this algorithm works as a local search which makes a narrow space for the search of a solution, rather than other algorithms which work as a global search

One main point presented in the article is That the algorithm presented by the author is not only more

Chapter 3, there is an overview on what is called the Fitnessgram and Coordinated Approach to

These algorithms have their own positives and negatives. New algorithms are evolving day by day. Two algorithms listed below are discussed in this essay.

We will refer to this grid as the input grid. Additionally, a SOM has a second grid, which, for this project, contains thirty-six randomized nodes close to the range of the data points. For us, each of the values is between zero and one. Each of these nodes is six dimensional in order to match our data points, so that now the thirty-six data points are located within the data space. We will refer to this grid as the map grid. Both of these sets of nodes are located in two separate matrixes. The two dimensional node and the six dimensional node located in the same row of the matrixes represent the same point. These randomized points relate to the ones in the static grid one-to-one, allowing us to keep the points in the randomized grid connected to one another in the same pattern that the nodes are connected in the static grid.

The solution of large scale MAED problem with the wind integration using backtracking search (BSA) algorithm presented in [12]. Each algorithm has its own advantage. But the key point associated with MFO algorithms which make them popular for solution of complex constrained problem in comparison to conventional approach are there is

The cluster boundary algorithm is divided in units (1) searching for the extreme genes in cluster (2) calculate the middle point of the extreme genes (3) calculate the mid-points between each extreme point-pairs (4) determine the radius of a ball with interior point in the cluster (p4).

The original PSO algorithm was developed by Kennedy and Eberhart. It was encouraged by swarming like schooling behavior in fish and flocking behavior in birds. Both the swarms do not process any global optimization but they provide a technique for confusing predators and predator

A common process of EC algorithms is as follows. Initially, the population are randomly scattered the search space. Along with the evolution process, the population

This technique is based on the popular k-means clustering algorithm. The clustering algorithm can work on data with many dimensions and target to reduce the distance within clusters. In the same time, it increases the distance between clusters. Initiating with K centers, the method iteratively assign each point to its nearest center dependent on

The convergence of estimated value in Eq. 2.1 and Eq. 2.2 to the actual value E can be assured with a suitable dynamic characteristic. It gives the following objective function :-

It is developed from swarm intelligence and is inspired by social behaviour of bird flocking or fish schooling. PSO is an optimization algorithm with implicit parallelism which can be easily handled with the non-differential objective functions. PSO algorithm uses a number of particle vectors moving around in the solution space searching for the optimist solution. Every particle in the algorithm acts as a point in the N-dimensional space. Each particle keeps the information in the solution space for each iteration and the best solution is calculated that has obtained by that particle is called personal best (pbest). This solution is obtained according to the personal experiences of each particle vector. Another best value that is tracked by the PSO is in the neighborhood of that particle and this value is called (gbest)

First we will define a cost function related to the NS. Then we will describe the important variables. After defining the cost function, the relevance of the cost function will be explained.

In PSO each member of the population is called particle, and each population is called a swarm.

Abstract— The set of objects having same characteristics are organized in groups and clusters of these objects are formed known as Data Clustering.It is an unsupervised learning technique for classification of data. K-means algorithm is widely used and famous algorithm for analysis of clusters.In this algorithm, n number of data points are divided into k clusters based on some similarity measurement criterion. K-Means Algorithm has fast speed and thus is used commonly clustering algorithm. Vector quantization,cluster analysis,feature learning are some of the application of K-Means.However results generated using this algorithm are mainly dependant on choosing initial cluster centroids.The main shortcome of this algorithm is to provide appropriate number of clusters.Provision of number of clusters before applying the algorithm is highly impractical and requires deep knowledge of clustering