An improved exponential metric space approach for C-mean clustering analysing
Rakesh Kumar
Department of Mathematics, Lovely Professional University, Phagwara, India
Search for more papers by this authorVarun Joshi
Department of Mathematics, Lovely Professional University, Phagwara, India
Search for more papers by this authorCorresponding Author
Gaurav Dhiman
Department of Computer Science, Government Bikram College of Commerce, Patiala, India
Correspondence
Gaurav Dhiman, Department of Computer Science, Government Bikram College of Commerce, Patiala, India.
Email: [email protected]
Search for more papers by this authorWattana Viriyasitavat
Business Information Technology Division, Department of Statistics, Faculty of Commerce and Accountancy, Chulalongkorn University, Bangkok, Thailand
Search for more papers by this authorRakesh Kumar
Department of Mathematics, Lovely Professional University, Phagwara, India
Search for more papers by this authorVarun Joshi
Department of Mathematics, Lovely Professional University, Phagwara, India
Search for more papers by this authorCorresponding Author
Gaurav Dhiman
Department of Computer Science, Government Bikram College of Commerce, Patiala, India
Correspondence
Gaurav Dhiman, Department of Computer Science, Government Bikram College of Commerce, Patiala, India.
Email: [email protected]
Search for more papers by this authorWattana Viriyasitavat
Business Information Technology Division, Department of Statistics, Faculty of Commerce and Accountancy, Chulalongkorn University, Bangkok, Thailand
Search for more papers by this authorAbstract
In this article, we present two resilient algorithms, the improved alternative hard c-means (IAHCM) and the improved alternative fuzzy c-means (IAFCM). We implement the Gaussian distance-dependent function proposed by Zhang and Chen (D.-Q. Zhang and Chen, 2004). In some cases, Zhang and Chen's metric distance does not account for the clustering centroid effect predicted by the large value. R* is employed in IAHCM and IAFCM to discover robust results while minimizing its sensitivity. Experiments are conducted using two-and three-dimensional data, including Diamond and Iris real-world data. The results are based on demonstrating the robust simplicity and applicability of the offered algorithms. Similarly, computational complexity is assessed.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
Open Research
DATA AVAILABILITY STATEMENT
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
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