Nonlinear Markov Clustering by Minimum Curvilinear Sparse Similarity
This work addresses the limitation of Markov clustering in handling nonlinear patterns, offering a domain-specific advancement for data science applications.
The authors tackled the problem of unsupervised pattern recognition in nonlinear data by proposing MC-MCL, the first nonlinear kernel extension of Markov clustering, which improved clustering performance over classical MCL and baseline methods like DBSCAN and K-means in various nonlinear datasets.
The development of algorithms for unsupervised pattern recognition by nonlinear clustering is a notable problem in data science. Markov clustering (MCL) is a renowned algorithm that simulates stochastic flows on a network of sample similarities to detect the structural organization of clusters in the data, but it has never been generalized to deal with data nonlinearity. Minimum Curvilinearity (MC) is a principle that approximates nonlinear sample distances in the high-dimensional feature space by curvilinear distances, which are computed as transversal paths over their minimum spanning tree, and then stored in a kernel. Here we propose MC-MCL, which is the first nonlinear kernel extension of MCL and exploits Minimum Curvilinearity to enhance the performance of MCL in real and synthetic data with underlying nonlinear patterns. MC-MCL is compared with baseline clustering methods, including DBSCAN, K-means and affinity propagation. We find that Minimum Curvilinearity provides a valuable framework to estimate nonlinear distances also when its kernel is applied in combination with MCL. Indeed, MC-MCL overcomes classical MCL and even baseline clustering algorithms in different nonlinear datasets.