Publication Title : I-nice: A new approach for identifying the number of clusters and initial cluster centres
Publicationed By : Dr. Md Abdul Masud
Publication Publication Date : 2018-10-06 00:00:00
Publication Online Link : https://www.sciencedirect.com/science/article/pii/S0020025517301135?via%3Dihub
Publication Description :
This paper proposes I-nice, which is a new method for automatically identifying the number of clusters and selecting the initial cluster centres in data. The method mimics a human being in observing peaks of mountains in field observation. The clusters in a dataset are considered as the hills in a field terrain. The distribution of distances between the observation point and the objects is computed. The distance distribution is modelled by a set of Gamma mixture models (GMMs), which are solved with the expectation-maximization (EM) algorithm. The best-fitted model is selected with an Akaike information criterion variant (AICc). In the I-niceSO algorithm, the number of components in the model is taken as the number of clusters, and the objects in each component are analysed with the k-nearest-neighbour method to find the initial cluster centres. For complex data with many clusters, we propose the I-niceMO algorithm, which combines the results of multiple observation points. Experimental results show that the two algorithms significantly outperformed two state-of-the-art methods (Elbow and Silhouette) in identifying the correct number of clusters in data. The results also show that I-niceMO improved the clustering accuracy and efficiency of the k-means clustering process.