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

K-MeansK-means clustering is the partitioning algorithm. K-means recreates each data in the dataset to only one of the new clusters formed. A data or data point is assigned to the adjacent cluster using a measure of distance or similarity.In k-means, an object is generated to the nearest center. It can define cannot-link constraints, and it modifies the center assignment process in k-means to the closest applicable center assignment.When the objects are created to centers in sequence, at each step it can provide the assignments so far do not disorganize some cannot-link constraints. An object is created to the closest center ... Read More

A hierarchical clustering approach operates by merging data objects into a tree of clusters. Hierarchical clustering algorithms are top-down or bottom-up. The feature of accurate hierarchical clustering techniques degenerates from its lack to perform adjustment because a merge or split decision is completed.There are various elements of hierarchical clustering which are as follows −Lack of a Global Objective FunctionAgglomerative hierarchical clustering methods use several elements to decide locally, at each step, which clusters must be merged (or split for divisive approaches).This method yields clustering algorithms that prevent the difficulty of undertaking to solve a complex combinatorial optimization problem.Ability to Handle ... Read More

Agglomerative Hierarchical clustering is a bottom-up clustering approach where clusters have sub-clusters, which consecutively have sub-clusters, etc. It starts by locating every object in its cluster and then combines these atomic clusters into higher and higher clusters until some objects are in a single cluster or until it needs a definite termination condition. Several hierarchical clustering approach are used to this type. They are distinct only in their description of between-cluster similarity.For example, a method called AGNES (Agglomerative Nesting) needs the single-link techniques and operates as follows. Consider there are groups of objects placed in a rectangle. Initially, each object ... Read More

The bisecting K-means algorithm is a simple development of the basic K-means algorithm that depends on a simple concept such as to acquire K clusters, split the set of some points into two clusters, choose one of these clusters to split, etc., until K clusters have been produced.The k-means algorithm produces the input parameter, k, and division a set of n objects into k clusters so that the resulting intracluster similarity is high but the intercluster analogy is low. Cluster similarity is evaluated concerning the mean value of the objects in a cluster, which can be viewed as the cluster’s ... Read More