Abstract
The membrane computing model, also known as the P system, is a parallel and distributed computing system. K-medoids algorithm is one of the most famous algorithms in partition-based clustering algorithms, and has been widely used in data analysis and modern scientific research. Combining the P system with the k-medoids algorithm, the maximum parallelism calculated by the P system can effectively reduce the time complexity of the k-medoids clustering algorithm. Based on this, this paper proposes a cell-like P system with promoters and inhibitors based on k-medoids clustering, and then an instance is given to illustrate the practicability and effectiveness of the P system designed.
Highlights
Membrane computing[1,2], which is initiated by Pun in 1998, is a branch of molecular computing
To deal with self-driven clustering problem, ref.[32] proposes a membrane clustering algorithm based on a tissue-like P system with fully connected structure to solve how many clusters is the most appropriate and what does a good clustering partitioning look like at the same time
This paper aims to obtain a P system Πkmbc for clustering based on k-medoids method
Summary
Abstract—The membrane computing model, known as the P system, is a parallel and distributed computing system. K-medoids algorithm is one of the most famous algorithms in partition-based clustering algorithms, and has been widely used in data analysis and modern scientific research. Combining the P system with the k-medoids algorithm, the maximum parallelism calculated by the P system can effectively reduce the time complexity of the k-medoids clustering algorithm. This paper proposes a cell-like P system with promoters and inhibitors based on k-medoids clustering, and an instance is given to illustrate the practicability and effectiveness of the P system designed
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