An efficient density-based clustering with side information and active learning: A case study for facial expression recognition task

Abstract

Data clustering is one of the most important tasks in machine learning and data mining, which aims to discover natural structure of the data, identify relationships between observations inside data sets, or detect outliers. Clustering is traditionally seen as part of unsupervised learning, but in many situations, side information about the clusters may be available in addition to the values of the features. For example, the cluster labels of some observations may be known (called seeds) or certain observations may be known to belong (or not) to the same cluster (pairwise constraints). Clustering algorithms using such information are called semi-supervised algorithms. A problem is that although many semi-supervised clustering algorithms have been presented in literature over the last decades, each of them usually uses one kind of side information. In this work, we aim to propose a new semi-supervised density based clustering which integrates effectively both kinds of side information, and embeds an active learning strategy in the process of finding clusters, named MCSSDBS. In order to evaluate our proposed method and demonstrate its effectiveness compared with a state-of-the-art semi-supervised density-based clustering (SSDBSCAN), a series of experiments is carried out on both synthetic and real world data sets. First is experiments primarily conducted on 6 data sets from UCI repository. Then, especially for the facial expression recognition task, our tests are performed on two facial data sets: A popular one in literature – the extended Cohn Kanade Data set (CK+), and our own new facial data set collected from volunteers in Vietnam – named ITI facial expression data set. Comparative results conducted show that our method can boost the performance of clustering process.