High-Dimensional Unsupervised Active Learning Method

Abstract

In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the data points as one-dimensional ink drop patterns, in order to summarize the effects of all data points, and then applies a threshold on the resulting vectors. It is based on an ensemble clustering method which performs one-dimensional density partitioning to produce ensemble of clustering solutions. Then, it assigns a unique prime number to the data points that exist in each partition as their labels. Consequently, a combination is performed by multiplying the labels of every data point in order to produce the absolute labels. The data points with identical absolute labels are fallen into the same cluster. The hierarchical property of the algorithm is intended to cluster complex data by zooming in each already formed cluster to find further sub-clusters. The algorithm is verified using several synthetic and real-world datasets. The results show that the proposed method has a promising performance, compared to some well-known high-dimensional data clustering algorithms.