Non-linear merge and split method for image clustering

Medical Image Segmentation with Split-and-Merge Method

The processing of microscopic tissue images and especially the detection of cell nuclei is nowadays done more and more using digital imagery and special immunodiagnostic software products. One of the most promising methods is region growing but it is quite memory intensive. The size of highresolution tissue images can easily reach the order of a hundred megabytes therefore the memory requiremen...

Split-and-Merge Method for Accelerating Convergence of Stochastic Linear Programs

Stochastic program optimizations are computationally very expensive, especially when the number of scenarios are large. Complexity of the focal application, and the slow convergence rate add to its computational complexity. We propose a split-and-merge (SAM) method for accelerating the convergence of stochastic linear programs. SAM splits the original problem into subproblems, and utilizes the ...

Split-and-merge Tweak in Cross Entropy Clustering

Minimum spanning tree based split-and-merge: A hierarchical clustering method

Most clustering algorithms become ineffective when provided with unsuitable parameters or applied to datasets which are composed of clusters with diverse shapes, sizes, and densities. To alleviate these deficiencies, we propose a novel split-and-merge hierarchical clustering method in which a minimum spanning tree (MST) and an MST-based graph are employed to guide the splitting and merging proc...

Unsupervised Semantic Role Induction via Split-Merge Clustering

In this paper we describe an unsupervised method for semantic role induction which holds promise for relieving the data acquisition bottleneck associated with supervised role labelers. We present an algorithm that iteratively splits and merges clusters representing semantic roles, thereby leading from an initial clustering to a final clustering of better quality. The method is simple, surprisin...