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 da...

Cluster analysis is a crucial technique in unsupervised machine learning, pattern recognition, and data analysis. However, current clustering algorithms suffer from the need for manual determination of parameter values, low accuracy, inconsistent performance concerning size structure. To address these challenges, novel algorithm called fully automated density-based method (FADBC) proposed. The ...

Clustering combination uses more than one clustering method with identical pattern features to improve the clustering performance. In general clustering is an optimization procedure based on a specific clustering criterion, so clustering combination can be regarded as a technique that constructs and processes multiple clustering criteria rather than a single criterion. We propose two methods of...

This paper presents a new hybrid fuzzy clustering method. In the proposed method, cluster prototypes are values that minimize the introduced generalized cost function. The proposed method can be considered as a generalization of fuzzy c-means (FCM) method as well as the fuzzy c-median (FCMed) clustering method. The generalization of the cluster cost function is made by applying the Lp norm. The...