多模糊化的可信安全半监督模糊聚类方法的改进

Tran Manh Tuan, Phung The Huan, Pham Huy Thong, T. Ngan, Le Hoang Son
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引用次数: 0

摘要

数据聚类应用于文档分类、牙科x射线图像分割、医学图像分割等多个领域。特别是,聚类算法在卫星图像处理中有许多重要的应用领域,包括参与交通、物流的车辆分类,以及对卫星图像进行分类预测干旱、洪水、森林火灾等。在采集卫星图像数据的过程中,有云层、天气、…这可能会影响图像质量。低质量的图像会使聚类算法的性能下降。除此之外,聚类算法中的模糊化参数也会影响聚类结果。过去,聚类方法通常使用相同的模糊化参数m = 2。但在实际中,每个元素都应该有自己的参数m。因此,确定参数m是提高模糊聚类性能的必要条件。本文提出了一种基于置信问题和多模糊指标的数据分割改进算法TS3MFCM。该方法由“标记数据的FCM”、“数据变换”和“多点模糊化半监督模糊聚类”三个步骤组成。实现了TS3MFCM方法,并与置信度加权安全半监督聚类(CS3FCM)进行了实验比较。在相同的数据集上,该方法在计算时间和聚类精度上都优于已有的方法
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AN IMPROVEMENT OF TRUSTED SAFE SEMI-SUPERVISED FUZZY CLUSTERING METHOD WITH MULTIPLE FUZZIFIERS
Data clustering are applied in various fields such as document classification, dental X-ray image segmentation, medical image segmentation, etc. Especially, clustering algorithms are used in satellite image processing in many important application areas, including classification of vehicles participating in traffic, logistics, classification of satellite images to forecast droughts, floods, forest fire, etc. In the process of collecting satellite image data, there are a number of factors such as clouds, weather, ... that can affect to image quality. Images with low quality will make the performance of clustering algorithms decrease. Apart from that, the parameter of fuzzification in clustering algorithms also affects to clustering results. In the past, clustering methods often used the same fuzzification parameter, m = 2. But in practice, each element should have its own parameter m. Therefore, determining the parameters m is necessary to increase fuzzy clustering performance. In this research, an improvement algorithm for the data partition with confidence problem and multi fuzzifier named as TS3MFCM is introduced. The proposed method consists of three steps namely as “FCM for labeled data”, “Data transformation”, and “Semi-supervised fuzzy clustering with multiple point fuzzifiers”. The proposed TS3MFCM method is implemented and experimentally compared against with the Confidence-weighted Safe Semi-Supervised Clustering (CS3FCM). The performance of proposed method is better than selected methods in both computational time and clustering accuracy on the same datasets
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