- 期刊
- OpenAccess
A New Optimized Data Clustering Technique using Cellular Automata and Adaptive Central Force Optimization (ACFO)
並列摘要
As clustering techniques are gaining more important today, we propose a new clustering technique by means of ACFO and cellular automata. The cellular automata uniquely characterizes the condition of a cell at a specific moment by employing the data like the conditions of a reference cell together with its adjoining cell, total number of cells, restraint, transition function and neighbourhood calculation. With an eye on explaining the condition of the cell, morphological functions are executed on the image. In accordance with the four stages of the morphological process, the rural and the urban areas are grouped separately. In order to steer clear of the stochastic turbulences, the threshold is optimized by means of the ACFO. The test outcomes obtained vouchsafe superb performance of the innovative technique. The accomplishment of the new-fangled technique is assessed by using additional number of images and is contrasted with the traditional methods like CFO (Central Force Optimization) and PSO (Particle Swarm Optimization).
延伸閱讀
- Siu, L. W. (1999). Application of cellular automata to one-dimensional density classification [master's thesis, The University of Hong Kong]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0029-1812201200006416
- 李侑政（2015）。An Integration of Particle Swarm Optimization Clustering Techniques for Collaborative Filtering〔碩士論文，中原大學〕。華藝線上圖書館。https://doi.org/10.6840/cycu201600156
- 吳宗和（2006）。Categorical Data Clustering Using Gravitational Algorithm and Randomized Technique〔碩士論文，國立臺灣大學〕。華藝線上圖書館。https://doi.org/10.6342/NTU.2006.01937
- Lee, T. P., & Kreng, V. B. (2008). A Nonparametric Multi-Seed Data Clustering Technique. 工業工程學刊, 25(1), 1-10. https://doi.org/10.29977/JCIIE.200801.0001
- 王友哲（2013）。A New Efficient and Effective Data Clustering Technique for Dynamic Image Analysis〔碩士論文，國立屏東科技大學〕。華藝線上圖書館。https://doi.org/10.6346/NPUST.2013.00253