CURE represents Clustering Using Representative. It is a clustering algorithm that uses a multiple techniques to make an approach that can manage high data sets, outliers, and clusters with non-spherical architecture and non-uniform sizes. CURE defines a cluster by using several representative points from the cluster.These points will taking the geometry and architecture of the cluster. The first representative point is selected to be the point farthest from the middle of the cluster, while the remaining points are selected so that they are farthest from all the earlier selected points. In this method, the representative points are associatively well distributed. ... Read More

The m by m proximity matrix for m data points can be defines as a dense graph in which each node is linked to some others and the weight of the edge between some group of nodes follow their pairwise proximity. Although each object has some method of similarity to each other object, for most data sets, objects are hugely same to a small number of objects and weakly same to most other objects.This feature can be used to sparsify the proximity graph (matrix), by setting some low-similarity (high-dissimilarity) values to 0 before starting the actual clustering process. The sparsification ... Read More

The process of combining a set of physical or abstract objects into classes of the same objects is known as clustering. A cluster is a set of data objects that are the same as one another within the same cluster and are disparate from the objects in other clusters. A cluster of data objects can be considered collectively as one group in several applications. Cluster analysis is an essential human activity.Clustering supports in identifying the outliers. The same values are organized into clusters and those values which fall outside the cluster are known as outliers. Clustering techniques consider data tuples ... Read More

A grid is an effective method to organize a set of data, minimum in low dimensions. The concept is to divide the applicable values of each attribute into a multiple contiguous intervals, making a set of grid cells. Each object declines into a grid cell whose equivalent attribute intervals include the values of the object.Objects can be created to grid cells in one pass through the record, and data about each cell, including the number of points in the cell, can also be gathered concurrently.There are multiple ways to implement clustering using a grid, but most methods are based on ... Read More

SOM represents Self-Organizing Feature Map. It is a clustering and data visualization technique depends on a neural network viewpoint. Regardless of the neural network basis of SOM, it is simply presented-minimum in the context of the alteration of prototype-based clustering.The algorithm of SOM is as follows −Initialize the centroids.repeatChoose the next object.Determine the closest centroid to the object.Refresh this centroid and the centroids that are close, i.e., in a definite neighborhood.until the centroids don't change much or a threshold is outspace.Create each object to its nearest centroid and restore the centroids and clusters.Initialization − This step (line 1) can be ... Read More

SOM represents Self-Organizing Feature Map. It is a clustering and data visualization approaches depends on a neural network viewpoint. The objective of SOM is to discover a set of centroids (reference vectors in SOM terminology) and to create each object in the data set to the centroid that supports the best closeness of that object. In neural network methods, there is one neuron related to each centroid.As with incremental K-means, data objects are phased one at a time and the nearest centroid is refreshed. Unlike K-means, SOM imposes a topographic sequencing on the centroids and nearby centroids are also upgraded. ... Read More

In prototype-based clustering, a cluster is a group of objects in which some object is nearer to the prototype that represents the cluster than to the prototype of some other cluster. A simple prototype-based clustering algorithm that needs the centroid of the elements in a cluster as the prototype of the cluster.There are various approaches of Prototype-Based clustering which are as follows −Objects are enabled to belong to higher than one cluster. Furthermore, an object belongs to each cluster with some weight. Such a method addresses the fact that some objects are similarly close to multiple cluster prototypes.A cluster is ... Read More

There are various characteristics of clustering algorithms which are as follows −Order Dependence − For several algorithms, the feature and number of clusters produced can vary, perhaps dramatically, based on the order in which the data is processed. While it can seem desirable to prevent such algorithms, sometimes the order dependence is associatively minor or the algorithm can have several desirable features.Non-determinism − Clustering algorithms, including K-means, are not order-dependent, but they make several results for each run because they based on an initialization step that needed a random choice. Because the feature of the clusters can vary from one ... Read More

The process of combining a set of physical or abstract objects into classes of the same objects is known as clustering. A cluster is a set of data objects that are the same as one another within the same cluster and are disparate from the objects in other clusters. A cluster of data objects can be considered collectively as one group in several applications. Cluster analysis is an essential human activity.Cluster analysis is used to form groups or clusters of the same records depending on various measures made on these records. The key design is to define the clusters in ... Read More

The following are some characteristics of data that can strongly affect cluster analysis which is as follows −High Dimensionality − In high-dimensional data sets, the traditional Euclidean concept of density, which is the several points per unit volume, becomes significant. It is considered that as the multiple dimensions increase, the volume increases growingly, and unless the multiple points grow exponentially with the multiple dimensions, the density tends to 0.It can also proximity influence to become more uniform in high-dimensional areas. There is another method to consider this fact is that there are more dimensions (attributes) that contribute to the proximity ... Read More