- I have a search for clustering algorithms. What is required of me is to choose five articles and write a summary or an abbreviation for each paper of 20 sentences for each research paper and answer...

Article 1
Clustering is the process that includes searching of pattern that exists in the form of data sets. It is the process that is considered for grouping data objects and is included with disjointed clusters so that data in the cluster are similar. These techniques are applied for application areas and it includes retrieval of information, recognization of pattern and analysis of data. K-Means is the clustering algorithm and it is considered as attractive practice as it is a simple way to partition an input dataset into k clusters. All clusters are represented with changing centroids and start with initial values. The k-means depends on the initial cluster and it depends on the initial configuration and it can be solved with a problem for proposing the algorithm that is used for computing the initial clusters. The genetic algorithms are developed with selecting centers for seeding the popular k-means methods that are used in clustering.
The selection of the correct cluster number includes two ways and the first way invokes heuristics approaches and the clustering algorithms that can run in for multiple times that includes number of multiple clusters. It can help in tackling the problem with appropriate selection for the number of clusters and it can help in addition to new mechanisms to FSCL. Every input data point provides the basic idea that not only the cluster center is the winner cluster that is modified for adapting the input data (Žalik, 2008). The metric automatically penalizes the rival cluster center in competition for getting new points in the cluster. The k-mean algorithm is initial for clustering and it is allocated with k cluster centers and every actual cluster can have more centers. It provides an input parameter that includes clusters that is greater than the actual number than performed by data.
The article does not define the use of machine learning in k-Means clustering. It is an important algorithm that can be applied successfully for problems that are classified. It is the key for the success of finding the exact and appropriate resolution for searching and optimization of problems. It is considered an iterative procedure that can help in maintaining a constant size population for providing feasible solutions during every step of iteration. It is based on an encoding scheme and it can help in representation of clustering of n objects as the strength of n integers. This provides a permutation representation of clustering and every row is corresponding to the cluster and every column is associated with the object. It is a representation of the need of local search for determining the way of clustering for representation of values.
The article can be extended with the information about the multiple methods that are used in k-Means clustering (Žalik, 2008). It includes the use of methods that can help in dividing a set of data into different clusters and every object belongs to one of these clusters. Every cluster can be represented as elements that can help in describing all objects that are contained within the cluster. It includes arithmetic mean of attributes for vectors of objects in the clusters. This requires determining clusters by addition of the closest pair of objects with a single cluster and it can help in determining 2 objects that are closest to each other.
Article 2
This article includes information about mode seeking, mean shift and clustering. Mean shift is an iterative procedure that can shift with each data point and it includes average point of data. The shifting of mode-seeking process on the surface is constructed for showing the shadow kernel. Mean shifting is the process that includes step size with the magnitude of gradients (Cheng, 1995). It is associated and it includes steepest aspect that is fixed with step size in the slow movement on the surface. The article is about a truncated kernel that is used for the blurring process and it can converge in many points.
The interesting part of this article is that it defines mean shift clustering and it includes the blurring process and it includes use of broad kernel and it uses data points to converge for the single point. The kernel is truncated with the degree for covering data point in position and it is the initial data for the fixed point that includes the blurring process. Iteration is the part of mean shift and it includes use of natural clustering for providing nearest center classification that can be used for deciding the data points...