Aiming at the problems of low convergence precision and premature convergence in Teaching-Learning-Based Optimization (TLBO) algorithms, an improved Self-Learning mechanism-based TLBO (SLTLBO) algorithm was proposed. A more complete learning framework was constructed for students in SLTLBO algorithm. Besides, after completing nomal learning in "teaching" and "learning" stage, students would further compare their differences from the teachers and the worst students, then various learning operations were implemented independently, so as to enhance their knowledge level and improve the convergence accuracy of the algorithm. Meanwhile, the students carried out self-examination through Gaussian searching to jump out of the local area and achieved better global search. The performance of SLTLBO was tested on 10 benchmark functions and compared with the algorithms including Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and TLBO. The experimental results verify the effectiveness of the proposed SLTLBO algorithm.

Some algorithms in pattern recognition and machine learning can only deal with discrete attribute values, while in real world many data sets consist of continuous data values. An unsupervised method was proposed according to the question of discretization. First, K-means method was employed to partition the data set into multiple subgroups to acquire label information, and then a supervised discretization algorithm was applied to the divided data set. When the process was repeatedly executed, multiple discrete results were obtained. These results were then integrated with an ensemble technique. Finally, the minimum sub-intervals were merged after priority dimensions and adjacent intervals were determined according to the neighbor relationship of data, where the number of sub-intervals was automatically estimated by preserving the correlation so that the intrinsic structure of the data set was maintained. The experimental results of applying categorical clustering algorithms such as spectral clustering demonstrate the feasibility and effectiveness of the proposed method. For example, its clustering accuracy improves by about 33% on average than other four methods. Discrete data attained can be used for some data mining algorithm, such as ID3 decision tree algorithm.