A semi-supervised sparse K-Means algorithm
This work addresses clustering challenges in scenarios with mixed data quality and limited supervision, offering an incremental improvement by combining sparse and semi-supervised techniques for domain-specific applications.
The paper tackles the problem of data clustering with unknown feature quality and limited labeled data by proposing a semi-supervised sparse K-Means algorithm that identifies informative features and uses constraints to enhance clustering. It shows the algorithm maintains high performance similar to other semi-supervised methods while preserving feature selection ability, tested on synthetic and real-world datasets with various constraints and initialization methods.
We consider the problem of data clustering with unidentified feature quality and when a small amount of labelled data is provided. An unsupervised sparse clustering method can be employed in order to detect the subgroup of features necessary for clustering and a semi-supervised method can use the labelled data to create constraints and enhance the clustering solution. In this paper we propose a K-Means variant that employs these techniques. We show that the algorithm maintains the high performance of other semi-supervised algorithms and in addition preserves the ability to identify informative from uninformative features. We examine the performance of the algorithm on synthetic and real world data sets. We use scenarios of different number and types of constraints as well as different clustering initialisation methods.