Approaches of Graph-Based Clustering

Ginni
Updated on 14-Feb-2022 12:59:00

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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

Algorithms of Grid-Based Clustering

Ginni
Updated on 14-Feb-2022 12:31:29

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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

What are the SOM Algorithm

Ginni
Updated on 14-Feb-2022 12:27:03

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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

What is SOM?

Ginni
Updated on 14-Feb-2022 12:20:16

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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

What is Prototype-Based Clustering

Ginni
Updated on 14-Feb-2022 12:18:15

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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

Characteristics of Clustering Algorithms

Ginni
Updated on 14-Feb-2022 12:16:41

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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

Elements of the Cluster

Ginni
Updated on 14-Feb-2022 12:14:45

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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

Data Characteristics

Ginni
Updated on 14-Feb-2022 12:13:01

3K+ Views

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

Difference Between K-Means and DBSCAN

Ginni
Updated on 14-Feb-2022 12:10:58

10K+ Views

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

Adobe Photoshop Photo and Design Software

Mahesh Parahar
Updated on 14-Feb-2022 12:00:07

2K+ Views

Adobe Photoshop is the most popular photo editor and designing software. Adobe Photoshop is available for Windows, macOS and iPAD. Adobe Photoshop is very easy to use and provides many useful features required for image editing and designing. Adobe Photoshop is an industry standard in photo editing and digital art. Adobe Photoshop is so popular that its trademark is a verb like to photoshop an image, photoshopping, photoshop contest etc. Adobe Photoshop is a proprietary software owned by Adobe Inc.Key FeaturesFollowing are the key features of Adobe Photoshop −Multiple Layers editing − Adobe photoshop allows to edit and develop raster ... Read More

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