The comparative study on agglomerative hierarchical clustering using numerical data. Issue 2 (December 2020)

• Record Type: Journal Article
• Authors: Praveen, P; Ranjith Kumar, M; Shaik, Mohammed Ali; Ravikumar, R; Kiran, R
• Is Part Of: IOP conference series. Volume 981:Issue 2(2020)
• Publication Date: 2020-12
• Subjects: Classification -- Clustering -- Data Mining -- Distance measure -- Hierarchical -- Linkage criteria -- Machine learning; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/981/2/022071
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: In theconventional way of convert data into a singleton or merging has many drawbacks mainly computational complexity. In this context hierarchical clustering method for quantitative measures of similarity among objects that could keep not only the structure of categorical attributes but also relative distance of numeric values. For numeric data the number of clusters can be validated through integral data, the hierarchical and partitioning methods the relationships among categorical items. In This Paper we hereinvestigate linkage criterions in hierarchical clustering algorithm performance calculations using with Euclidian distance measure and some clustering techniques and their applications have been discussed. It also describes the necessities to be calculated for constructing a well-organized to handle the huge data sets. As the study initially investigates distinct issues for creating clusters with numeric attributes. The efficiency is obtained by clustering of datasets that comprises of numeric attributes related to distinct applications. Significant issues such merging object naturally uses Euclidean distance is resolved by using Agglomerative methods.