Abstract
To address the issue that the K-means algorithm chooses and determines the initial cluster center in a random way, which would fall into the local optimal clustering result, a way towards choosing the initial clustering center using information entropy is proposed. This proposed method divides the dataset evenly into data blocks with more than K, and then uses the entropy method to obtain the value of target function of each data block, as well as selects the centroid corresponding to the data block with the smallest value function of the first k target as the initial cluster center. By using entropy method to ensure the efficiency of the initial clustering center selection, an anomaly detection method is proposed. The result of the experiment show that this method performs better than the traditional K-means algorithm both in clustering effect and anomaly detection ability.
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Acknowledgements
This paper is supported in part by the National Natural Science Foundation of China under Grant No. 61672022, Key Disciplines of Computer Science and Technology of Shanghai Polytechnic University under Grant No. XXKZD1604, and the Graduate Innovation Program No. A01GY17F022.
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Tan, W., Fang, X., Zhao, L., Tang, A. (2019). Anomaly Detection Algorithm Based on Cluster of Entropy. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_26
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DOI: https://doi.org/10.1007/978-981-13-3044-5_26
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