Mohsen Eid¹, Tamer Medhat^2,*, Manal E. Ali³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1531-1544, 2023, DOI:10.32604/csse.2023.031745

Abstract An information system is a type of knowledge representation, and attribute reduction is crucial in big data, machine learning, data mining, and intelligent systems. There are several ways for solving attribute reduction problems, but they all require a common categorization. The selection of features in most scientific studies is a challenge for the researcher. When working with huge datasets, selecting all available attributes is not an option because it frequently complicates the study and decreases performance. On the other side, neglecting some attributes might jeopardize data accuracy. In this case, rough set theory provides a useful approach for identifying superfluous… More >

Yunping Zhao¹, Weixuan Liang¹, Jianzhuang Lu^1,*, Xiaowen Chen¹, Nijiwa Kong²

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2709-2722, 2021, DOI:10.32604/cmc.2021.013389

Abstract Late fusion multi-view clustering (LFMVC) algorithms aim to integrate the base partition of each single view into a consensus partition. Base partitions can be obtained by performing kernel k-means clustering on all views. This type of method is not only computationally efficient, but also more accurate than multiple kernel k-means, and is thus widely used in the multi-view clustering context. LFMVC improves computational efficiency to the extent that the computational complexity of each iteration is reduced from O(n³) to O(n) (where n is the number of samples). However, LFMVC also limits the search space of the optimal solution, meaning that… More >