Bio Inspired Dynamic Data Clustering

Akash Shrivastava

ISBN 10: 1916706541 / ISBN 13: 9781916706545

Published by Self Publish, 2023

About this Item

Edition 2023. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Edition 2023. Ammareal gives back up to 15% of this item's net price to charity organizations.

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Bibliographic Details

• Title: Bio Inspired Dynamic Data Clustering
• Publisher: Self Publish
• Publication Date: 2023
• Language: English
• ISBN 10: 1916706541
• ISBN 13: 9781916706545
• Binding: Softcover
• Condition: Bon

About this title

Synopsis

In the era, where an enormous amount of data is getting generated from various  

resources in different formats, it is highly required to categorize this data in proper format to 

process useful knowledge which could be utilized effectively. Clustering technique is one of the 

effective and popular techniques to segregate data by abstracting underlying structure of  

the data. This approach is used to organize the data either to form a group of  

individuals or categorize as a hierarchy of groups. Clustering becomes an important technique 

to analyze large amounts of data which is frequently applied in various domains of  

engineering, science and other well-known areas such as biology, marketing, psychology,  

medicine, remote sensing, computer vision etc. The representation of data that has been  

done in clustering analysis is then undergone the observation. It is done to articulate and 

justify the grouping of data. The investigation is carried out to see whether the phenomenon  

of clustering is fitting into the preconceived ideas and experiments. 

Data mining is the domain where data is being retrieved, processed and transformed  

into information. In data mining, clustering is one of the most frequently used forms of 

exploratory data analysis which belongs to unsupervised classification of patterns  

 into groups .Clustering works as to divide data into groups on the basis of similarity 

and dissimilarity . It is the collection of those data sets and entities which lies in these 

groups pertaining to similar and different properties.

In most of the cases, clusters are formed by exploring their internal homogeneous properties and 

external separation of dataset. In prescribed clusters, patterns are found to be similar in the 

same groups and different in different groups . Data analysis belongs to many  

computing applications; it is considered to be involved in the design phase or as a  

part of their online functions. Data analysis procedures can be categorized as either  

exploratory or confirmatory, based on the models which are appropriate for the source of the 

data, but a key element in both types of processes is considered to be grouping, or classification of measurements.