JRM Vol.31 No.2 pp. 339-347
doi: 10.20965/jrm.2019.p0339


Uncertain Interval Data EFCM-ID Clustering Algorithm Based on Machine Learning

Yimin Mao*, Yinping Liu*, Muhammad Asim Khan*, Jiawei Wang*, Dinghui Mao**, and Jian Hu***

*Information Institute, Jiangxi University of Science and Technology
Hakka Avenue No.156, Zhanggong District, Ganzhou, Jiangxi 341000, China

**211 Battalion Co., Ltd., China Shanxi Nuclear Industry Group Company
Xi’an 710024, China

***Applied Science Institute, Jiangxi University of Science and Technology
Hakka Avenue No.156, Zhanggong District, Ganzhou, Jiangxi 341000, China

August 28, 2018
February 27, 2019
April 20, 2019
uncertain cluster, interval data, FCM, machine learning, density based
Uncertain Interval Data EFCM-ID Clustering Algorithm Based on Machine Learning

Data points in the skewed distribution

In clustering problems based on fuzzy c-means (FCM) for uncertain interval data, points within the interval are usually assumed to have uniform distribution, resulting in the difficulty of accurately describing the interval. Furthermore, the clustering results are considerably affected by the initial clustering centers, and the update speed of the membership degree is slow. To address these problems, a new clustering algorithm called uncertain FCM for interval data (EFCM-ID) is presented. On the basis of a quartile, a median quartile-spacing distance measurement for generally distributed interval data based on machine learning is designed to precisely determine these data. Simultaneously, we sample the whole dataset and consider the density centers as the initial clustering centers to increase accuracy. We call this method samplingbased density-center selection (SDCS). To reduce the running time, a new measurement based on competitive-learning theory to update the membership is developed. It accelerates the update speed by different degrees according to value of the membership degree. Experiments conducted on synthetic interval datasets show the feasibility of EFCM-ID.

Cite this article as:
Y. Mao, Y. Liu, M. Khan, J. Wang, D. Mao, and J. Hu, “Uncertain Interval Data EFCM-ID Clustering Algorithm Based on Machine Learning,” J. Robot. Mechatron., Vol.31, No.2, pp. 339-347, 2019.
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Last updated on May. 20, 2019