PIFHC: The Probabilistic Intuitionistic Fuzzy Hierarchical Clustering Algorithm

Hierarchical clustering techniques help in building a tree-like structure called dendrogram from the data points which can be used to find the closest related data objects. This paper presents a novel hierarchical clustering technique which considers intuitionistic fuzzy sets to deal with the uncertainty present in the data. Instead of using traditional hamming distance or Euclidean distance measure to find the distance between the data points, it employs the probabilistic Euclidean distance measure to propose a novel clustering approach which we term as ‘Probabilistic Intuitionistic Fuzzy Hierarchical Clustering (PIFHC) Algorithm’. The proposed PIFHC algorithm considers probabilistic weights from the data to measure the distances between the data points. Clustering results over UCI datasets show that our proposed PIFHC algorithm gives better cluster accuracies than its existing counterparts. PIFHC efficiently provides improvements of 1%–3.5% in the clustering accuracy compared to other fuzzy hierarchical clustering algorithms for most of the datasets. We further provide experimental results with the real-world car dataset and the Listeria monocytogenes dataset for mouse susceptibility to demonstrate the practical efficacy of the proposed algorithm. For Listeria datasets as well, proposed PIFHC records 1.7% improvement against the state-of-the-art methods The dendrograms formed by the proposed PIFHC algorithm exhibits high cophenetic correlation coefficient with an improvement of 0.75% over others. We provide various AGNES methods to update the distance between merged clusters in the proposed PIFHC algorithm. • This paper presents a novel hierarchical clustering approach based on intutionistic fuzzy sets. • The proposed approach is termed as ‘probabilistic intuitionistic fuzzy hierarchical clustering (PIFHC)” algorithm. • PIFHC employs probabilistic Euclidean distance measure with different probabilistic weights for its different components. • Also presents methods to compute the distances of the merged cluster from other clusters. • Conducts extensive experiments over a number of benchmark and real-world datasets to demonstrate PIFHC’s superiority over others.