Assessment of the Clusterability of Data Using a Multimodal Convolutional Neural Network

Abstract

Clustering assigns data points into groups called clusters, which define the characteristics of similar data points. Our work defines a model to identify and assess the presence of a clusterable structure initially in a two-dimensional density grid of a dataset, which is respectively expanded into a multidimensional density grid according the dimensionality of the dataset. Clusterability is defined as the tendency of a dataset having a structure for successful clustering. Our approach consists of a multimodal convolutional neural network to assess the clusterability of a dataset. Multimodality is the utilization of multiple sources of information. The output of our approach, the created model, also identifies the type of the clusterable structure (none, centroid, and density). Our approach does not require an initial clustering of the data to define its clusterability. In the assessment of the clusterability of high-dimensional data, we utilize random rotations accompanied with an ensemble approach. The multiple experiments of various clustering problems illustrate that our proposed approach is capable of assessing the clusterability of data and of identifying the type of the clusterable structure.