Recent Approaches to High-Dimension Data Grouping and Specific Hierarchical Grouping Techniques

Abstract

Purpose: Grouping of data in various clusters as per the similarities and differences is the main aim of designing this research. Clustering techniques helps in understanding the utility of the groups used in variety of fields such as brain research, social and behavioural science, Artificial intelligence and data exploration. Different techniques of clustering such as specific hierarchial clustering, concept of curse of dimensionality, conceptual spaces, and many more helps to understand the type of data and also its segregation with respect to the particular group. The aim of the research is to enlighten the concept broadly and how other researchers can use them in analysing the data. The techniques of clustering helps to choose any for further analysis of the data collected with the help of questionnaire.

Design/Methodology/Approach: The proposed research is descriptive and reviewed. The past researches helps to build the models and figures for better understanding of research.

Findings: The findings revealed that clustering is used not specifically in one research but also in various fields. Grouping of data collected for analysis in various clusters need a better clearance of clustering.

Originality/value: this research will be useful for all the researchers in different fields to analyse complex and big data to understand data mining, clustering and segregate data based on their similarities and differences.

Paper Type: Case Based Study