Abstract
This paper presents an efficient algorithm called K-harmonic means clustering algorithm with simulated annealing, for reducing the dependence of the initial values and overcoming to converge to local minimum. The proposed algorithm works by that K-harmonic means algorithm solves the problem that clustering result is sensitive to the initial valves and simulated annealing makes the clustering jump out of local optimal solution at each iteration patterns. The clustering result is verified by experiments on analyzing IRIS dataset. The school XunTong is application software that is convenient to communication between parents and teachers. This paper applies the new algorithm to analysis of dataset in School XunTong and finds the relationship of students’ achievement and the communication between parents and teachers. Finally, the result of classification guides the learning direction of students in universities and cultivates to students.
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Liu, Gl., Tan, Yq., Yu, Lm., Liu, J., Gao, Jq. (2013). Application Research of Modified K-Means Clustering Algorithm. In: Qi, E., Shen, J., Dou, R. (eds) The 19th International Conference on Industrial Engineering and Engineering Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38427-1_134
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DOI: https://doi.org/10.1007/978-3-642-38427-1_134
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