Comparison_of_clustering_algorithm
Clustering techniques are unsupervised learning methods of grouping similar from dissimilar data types. Therefore, these are popular for various data mining and pattern recognition purposes. However, their performances are data dependent. Thus, choosing right clustering technique for a given dataset is a research challenge. There are many clustering algorithms. The objective of this project is to perform a comparative analysis of four clustering algorithms namely Kmeans algorithm, Hierarchical algorithm, Density based algorithm and Fuzzy c-mean algorithm. These algorithms are compared in terms of efficiency and accuracy and do implementation in C language. The algorithms undertaken are K-Mean, Hierarchal, Fuzzy-C Mean, DBSCAN.
