Jili Chen^1,2, Hailan Wang², Xiaolan Xie^1,2,*

Computer Systems Science and Engineering, Vol.48, No.3, pp. 645-663, 2024, DOI:10.32604/csse.2023.037957

Abstract Fuzzy C-Means (FCM) is an effective and widely used clustering algorithm, but there are still some problems. considering the number of clusters must be determined manually, the local optimal solutions is easily influenced by the random selection of initial cluster centers, and the performance of Euclid distance in complex high-dimensional data is poor. To solve the above problems, the improved FCM clustering algorithm based on density Canopy and Manifold learning (DM-FCM) is proposed. First, a density Canopy algorithm based on improved local density is proposed to automatically deter-mine the number of clusters and initial cluster… More >

Kuan-Peng Chen^1,3, An-Cheng Yang^1,3, Wen-Jay Lee¹, Yi-Ming Tseng², Nien-Ti Tsou², Nan-Yow Chen^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.3, pp. 54-54, 2019, DOI:10.32604/icces.2019.05401

Abstract Crystallographic classification of microstructure is a very important issue in material science especially numerous data were generated by experiments or Molecular Dynamic (MD) simulations. Some analysis tools were purposed, such as coordination analysis and Honeycutt-Anderson (HA) pair analysis [1], however, to analyze these huge amounts of data is still quite difficult. Sometimes, crystallography prior knowledge of their structures is also desired in the classification procedures. Not only the task is very labor intensive but also the result is susceptible to errors and is usually lack of objectivity. In this study, we developed a computational workflow… More >