Combining hierarchical clustering approaches using the PCA method

In expert systems, data mining methods are algorithms that simulate humans' problem-solving capabilities. Clustering methods as unsupervised machine learning methods are crucial approaches to categorize similar samples in the same categories. The use of different clustering algorithms to a given dataset produces clusters with different qualities. Hence, many researchers have applied clustering combination methods to reduce the risk of choosing an inappropriate clustering algorithm. In these methods, the outputs of several clustering algorithms are combined. In these research works, the input hierarchical clusterings are transformed to descriptor matrices and their combination is achieved by aggregating their descriptor matrices. In previous works, only element-wise aggregation operators have been used and the relation between the elements of each descriptor matrix has been ignored. However, the value of each element of the descriptor matrix is meaningful in comparison with its other elements. The current study proposes a novel method of combining hierarchical clustering approaches based on principle component analysis (PCA). PCA as an aggregator allows considering all elements of the descriptor matrices. In the proposed approach, basic clusters are made and transformed to descriptor matrices. Then, a final matrix is extracted from the descriptor matrices using PCA. Next, a final dendrogram is constructed from the matrix that is used to summarize the results of the diverse clustering. The experimental results on popular available datasets show the superiority of the clustering accuracy of the proposed method over basic clustering methods such as single, average and centroid linkage and previously combined hierarchical clustering methods. In addition, statistical tests show that the proposed method significantly out-performed hierarchical clustering combination methods with element-wise averaging operators in almost all tested datasets. Several experiments have also been conducted which confirm the robustness of the proposed method for its parameter setting. (C) 2019 Elsevier Ltd. All rights reserved.