An algorithm for Non-negative Tensor Factorisation is introduced which extends current matrix factorisation techniques to deal with tensors. The effectiveness of the algorithm is then demonstrated through tests on synthetic data. The algorithm is then employed as a means of performing sound source separation on two channel mixtures, and the separation capabilities of the algorithm demonstrated ...

Aiming at the problems that Krill Herd (KH) algorithm is easy to fall into local optimum, searchability weak, and k-means easily affected by selection of initial clustering centre, a based on improved KH proposed. The initialized chaos, dynamic clustering, elite leadership random mutation strategies improve introduce optimal number adaptive mechanism, which enhances comprehensive optimization a...

In this article I summarize the main points I made in the keynote presentation of the same title I gave at the EURO XXIV conference in Lisbon, Portugal in July of 2010. Each of these points deals in some way with making communications between an operations research professional (academic or practitioner) and a student, client, subordinate, supervisor, or colleague more effective. Furthermore, e...

In this paper, we propose a new clustering algorithm to cluster data. The proposed algorithm adopts a new non-metric measure based on the idea of “symmetry”. The detected clusters may be a set of clusters of different geometrical structures. Three data sets are tested to illustrate the effectiveness of our proposed algorithm.

The Cooperative Target Observation (CTO) problem has been of great interest in the multi-agents and robotics literature due to the problem being at the core of a number of applications including surveillance. In CTO problem, the observer agents attempt to maximize the collective time during which each moving target is being observed by at least one observer in the area of interest. However, mos...

In this paper, we evaluate the player segmentation for trajectory estimation in soccer games. In order to estimate the field trajectories of players in soccer games, we should accurately locate the foot positions of players in each soccer image and transform them into those in the soccer field. However, we cannot always segment the players completely, since players are often motion-blurred due ...

Cluster analysis is a fundamental technique for various filed such as pattern recognition, machine learning and so forth. However, the cluster number is predefined by users in K-means algorithm, which is unpractical to implement. Since the number of clusters is a NP-complete problem, Genetic Algorithm is employed to solve it. In addition, due to the large time consuming in conventional method, ...

Through comparison and analysis of clustering algorithms, this paper presents an improved Kmeans clustering algorithm. Using genetic algorithm to select the initial cluster centers, using Z-score to standardize data, and take a new method to evaluate cluster centers, all this reduce the affect of isolated points, and improve the accuracy of clustering. Experiments show that the algorithm to fin...

background: in this paper we compare a highly accurate supervised to an unsupervised technique that uses breast thermal images with the aim of assisting physicians in early detection of breast cancer. methods: first, we segmented the images and determined the region of interest. then, 23 features that included statistical, morphological, frequency domain, histogram and gray-level co-occurrence ...

In this paper, the well-known k-means algorithm for searching for a locally 12 optimal partition of the setA ⊂ R is analyzed in the case if some data points occur on the 13 border of two or more clusters. For this special case, a useful strategy by implementation 14 of the k-means algorithm is proposed. 15