Clustering Analysis for Appropriate Crop Prediction using Hierarchical, Fuzzy C-Means, K-Means and Model based Techniques

Abstract

Data mining is a specific field of computer and information science with substantial point of view of
knowledge discovery from expansive database or dataset. Different types of techniques are accessible under data mining
and clustering or the unsupervised learning specifically. Clustering is a division of information into identical groups
each comparative gathering is known as a cluster. Object in a group are comparative or near each other. Various
methodologies are designed to implement the clustering technique very effectively. This paper represents a study on
different clustering techniques that are incorporated on the seed data sets to enhance the clustering approach based on
the various parameters like area, perimeter, compactness, length and width of the kernel, asymmetric coefficient and
length of the kernel groove.