A Comparative Analysis of Fuzzy C-Means Clustering and Harmonic K Means Clustering Algorithms
Data clustering is a common technique for data scrutiny, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis, video analysis and bioinformatics. Harmonic K Means algorithms (HKM) is a method of clustering which allows one piece of data to belong to two or more clusters. This study deals with, two of the most representative clustering algorithms namely Harmonic K Means algorithm (HKM) and fuzzy C means algorithm. They are described and analyzed based on the basic approach using the distance between two data points.