基于半监督K-Means的属性加权聚类算法

摘要

K-Means是经典的非监督聚类算法,因其速度快,稳定性高广泛应用在各个领域.但传统的K-Means没有考虑无关属性以及噪声属性的影响,并且不能自动寻找聚类数目K.而目前K-Means的改进算法中,也鲜有关于高维以及噪声方面的改进.因此,结合PCA提出基于半监督的K-Means加权属性聚类方法.首先,用PCA得到更少更有效的特征,并计算它们的分类贡献率(即每个特征对聚类的影响因子).其次,由半监督自适应算法得到K.最后将加权数据集以及K应用到聚类中.实验表明,该算法具有更好的识别率和普适性.%K-Means is a classic unsupervised clustering algorithm which is widely applied in various fields for its high speed and high stability.However, the traditional K-Means methods do not take unrelated attributes and the impact of noise into consideration.They also cannot automatically look for the number of clusters K.At present, the improved K-Means algorithms also rarely focus on high-dimensional data and noise attributes.Therefore, this paper proposes an attribute-weighted clustering algorithm based on semi-supervised K-Means associated with PCA.Firstly, the dimension reduction is achieved by introducing PCA, and the contribution rate of each dimension classification characteristics (the impact factor of clustering processed by each feature attribute) is calculated.Secondly, the number of clusters K is obtained through an adaptive semi-supervised algorithm.Finally, the weighted data sets and K are applied to clustering.Experimental results show that the proposed method has better recognition rate and universality.