A New semi-supervised clustering for incomplete data

Semi-supervised clustering technique partitions the unlabeled data based on prior knowledge of labeled data. Most of the semi-supervised clustering algorithms exist only for the clustering of complete data, i.e., the data sets with no missing features. In this paper, an effort has been made to check the effectiveness of semi-supervised clustering when applied to incomplete data sets. The novelty of this approach is that it considers the missing features along with available knowledge (labels) of the data set. The linear interpolation imputation technique initially imputes the missing features of the data set, thus completing the data set. A semi-supervised clustering is now employed on this complete data set, and missing features are regularly updated within the clustering process. In the proposed work, the labeled percentage range used is 30, 40, 50, and 60% of the total data. Data is further altered by arbitrarily eliminating certain features of its components, which makes the data incomplete with partial labeling. The proposed algorithm utilizes both labeled and unlabeled data, along with certain missing values in the data. The proposed algorithm is evaluated using three performance indices, namely the misclassification rate, random index metric, and error rate. Despite the additional missing features, the proposed algorithm has been successfully implemented on real data sets and showed better/competing results than well-known standard semi-supervised clustering methods.

Automated summary

This paper investigates the effectiveness of semi-supervised clustering on incomplete data sets. The approach combines missing features with available knowledge for data processing and applies semi-supervised clustering algorithm for analysis. The results show that despite the presence of missing features, the proposed algorithm performs better than standard methods on real data sets.