Non-Parametric Probabilistic Image Segmentation

We propose a simple probabilistic generative model for image segmentation. Like other probabilistic algorithms (such as EM on a Mixture of Gaussians) the proposed model is principled, provides both hard and probabilistic cluster assignments, as well as the ability to naturally incorporate prior knowledge. While previous probabilistic approaches are restricted to parametric models of clusters (e.g., Gaussians) we eliminate this limitation. The suggested approach does not make heavy assumptions on the shape of the clusters and can thus handle complex structures. Our experiments show that the suggested approach outperforms previous work on a variety of image segmentation tasks

DSMK-means “Density-based Split-and-Merge K-means clustering Algorithm”

Clustering is widely used to explore and understand large collections of data. K-means clustering method is one of the most popular approaches due to its ease of use and simplicity to implement. This paper introduces Density-based Split-and-Merge K-means clustering Algorithm (DSMK-means), which is developed to address stability problems of standard K-means clustering algorithm, and to improve the performance of clustering when dealing with datasets that contain clusters with different complex shapes and noise or outliers. Based on a set of many experiments, this paper concluded that developed algorithms “DSMK-means” are more capable of finding high accuracy results compared with other algorithms especially as they can process datasets containing clusters with different shapes, densities, or those with outliers and noise

Mapping Topographic Structure in White Matter Pathways with Level Set Trees

Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. We show that level set trees---which provide a concise representation of the hierarchical mode structure of probability density functions---offer a statistically-principled framework for visualizing and analyzing topography in fiber streamlines. Using diffusion spectrum imaging data collected on neurologically healthy controls (N=30), we mapped white matter pathways from the cortex into the striatum using a deterministic tractography algorithm that estimates fiber bundles as dimensionless streamlines. Level set trees were used for interactive exploration of patterns in the endpoint distributions of the mapped fiber tracks and an efficient segmentation of the tracks that has empirical accuracy comparable to standard nonparametric clustering methods. We show that level set trees can also be generalized to model pseudo-density functions in order to analyze a broader array of data types, including entire fiber streamlines. Finally, resampling methods show the reliability of the level set tree as a descriptive measure of topographic structure, illustrating its potential as a statistical descriptor in brain imaging analysis. These results highlight the broad applicability of level set trees for visualizing and analyzing high-dimensional data like fiber tractography output

Analysis of Mass Based and Density Based Clustering Techniques on Numerical Datasets

Clustering is the techniques adopted by data mining tools across a range of application . It provides several algorithms that can assess large data set based on specific parameters & group related points  . This paper gives comparative analysis of density based clustering algorithms and mass based clustering algorithms. DBSCAN [15] is a base algorithm for density based clustering techniques. One of the advantages of using these techniques is that method does not require the number of clusters to be given a prior and it can detect the clusters of different shapes and sizes from large amount of data which contains noise and outliers. OPTICS [14] on the other hand does not produce a clustering of a data set explicitly, but instead creates an augmented ordering of the database representing its density based clustering structure. Mass based clustering algorithm   mass estimation technique is used (it is alternate of density based clustering) .In Mass based clustering algorithm [22] there are also core regions and noise points are used as a parameter. We analyze the algorithms in terms of the parameters essential for creating meaningful clusters. All the algorithms are tested using numerical data sets for low as well as high dimensional data sets. Keywords: Mass Based (DEMassDBSCAN) ,DBSCAN,OPTICS

Semi-supervised model-based clustering with controlled clusters leakage

In this paper, we focus on finding clusters in partially categorized data sets. We propose a semi-supervised version of Gaussian mixture model, called C3L, which retrieves natural subgroups of given categories. In contrast to other semi-supervised models, C3L is parametrized by user-defined leakage level, which controls maximal inconsistency between initial categorization and resulting clustering. Our method can be implemented as a module in practical expert systems to detect clusters, which combine expert knowledge with true distribution of data. Moreover, it can be used for improving the results of less flexible clustering techniques, such as projection pursuit clustering. The paper presents extensive theoretical analysis of the model and fast algorithm for its efficient optimization. Experimental results show that C3L finds high quality clustering model, which can be applied in discovering meaningful groups in partially classified data