Corresponding Author: Athraa Jasim Mohammed School of Computing, Universiti Utara Malaysia, Kedah, Malaysia Email: [email protected] Abstract: Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters. Various efforts have been put to address such drawback and this includes...

The health industry is facing increasing challenge with “big data” as traditional methods fail to manage the scale and complexity. This paper examines clustering of patient records for chronic diseases to facilitate a better construction of care plans. We solve this problem under the framework of subspace clustering. Our novel contribution lies in the exploitation of sparse representation to di...

The application of optimization algorithms to parameter driven simulations and agents has been thoroughly explored in literature. However, classical optimization algorithms do not take into account the fact that simulations normally have dynamic scenarios. This paper analyzes the possibility of using the classical optimization methods, combined with clustering techniques, in order to optimize p...

We describe a new optimization scheme for finding highquality clusterings in planar graphs that uses weighted perfect matching as a subroutine. Our method provides lower-bounds on the energy of the optimal correlation clustering that are typically fast to compute and tight in practice. We demonstrate our algorithm on the problem of image segmentation where this approach outperforms existing glo...

We explore how techniques that were developed to improve the training process of artificial neural networks can be used to speed up fuzzy clustering. The basic idea of our approach is to regard the difference between two consecutive steps of the alternating optimization scheme of fuzzy clustering as providing a gradient, which may be modified in the same way as the gradient of neural network ba...

Clustering is one of the widely using data mining technique that is used to place data elements into allied groups of “similar behavior”. The conventional clustering algorithm called K-Means algorithm has some well-known problems, i.e., it does not work properly on clusters with not well defined centers, it is difficult to choose the number of clusters to construct different initial centers can...

We propose techniques of convex optimization for information theoretical clustering. The clustering objective is to maximize the mutual information between data points and cluster assignments. We formulate this problem first as an instance of max k cut on weighted graphs. We then apply the technique of semidefinite programming (SDP) relaxation to obtain a convex SDP problem. We show how the sol...

Most web search results clustering (SRC) strategies have predominantly studied the definition of adapted representation spaces to the detriment of new clustering techniques to improve performance. In this paper, we define SRC as a multi-objective optimization (MOO) problem to take advantage of most recent works in clustering. In particular, we define two objective functions (compactness and sep...

Numerical methods of solving the inverse light scattering problem for spheres are presented. The methods are based on two stochastic global optimization techniques: Deep’s random search and the multilevel single-linkage clustering analysis due to Rinnooy Kan and Timmer. Computational examples show that the radius and the refractive index of spheres comparable with or larger than the wavelength ...

Feature selection is the process of removing the irrelevant features from the datasets and fuzzy clustering of microarray data are the most fascinating machine learning techniques in the real world. The main objective of this paper is selecting the independent components of the microarray data using Independent Component Analysis in order to improve the effectiveness and accuracy of the Fuzzy P...