Authors

1. Ms Meenal Sharma

Affiliation

• Assistant Professor, International School of Informatics & Management, Jaipu

Abstract

Data mining is a technique used for retrieving information and data from the database. This paper
focuses on clustering, a technique which is used to divide the data into interdependent clusters that
are of similar type. Clustering is a way of classifying and breaking down the data, and putting the
similar type of meaningful data/objects into a single group or cluster. The technique follows the most
common approach of finding centre of clusters and input vectors, which help to identify which cluster
centre belongs to which input vector. Hierarchical clustering is a technique of clustering the data
using predetermined way of clustering, either it is top to bottom or it is bottom up approach. It is
further divided into two ways: Agglomerative (Bottom-up approach) and Divisive(Top-down
approach). K-Means clustering is a technique which makes various partitions of the given data from n
observations of objects into k clusters, where each of the object belongs to a particular cluster of
nearest mean(also known as center of clusters).
In this paper road accidents dataset is used that contains the records of road accidents occurred in
the India on day to day basis. The dataset holds the records of the accidents happened in each state
of India. This dataset is used because the number of accidents happening daily in the India is very big
in count and the accidents happen are of different types like fatal accidents, major accidents, noninjury
accidents, etc. This paper uses clustering algorithm to differentiate the categories of accidents
in the dataset. (Ambigavathi, M., & Sridharan, D. 2020).

Keywords

Data Clustering, Unsupervised learning technique, Hierarchical clustering, K-means, Statistical tool R, Clustering techniques

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