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 ...

The K-Means clustering is by far the most widely used method for discovering clusters in data. It has a good performance on the data with compact super-sphere distributions, but tends to fail in the data organized in more complex and unknown shapes. In this paper, we analyze in detail the characteristic property of data clustering and propose a novel dissimilarity measure, named density-sensiti...

Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the K-Means algorithm is implemented by three distance functions and to identify the optimal distance function for clustering methods. The proposed K-Means algorithm is compared with K-Means, S...

Clustering in data mining is very important to discover distribution patterns and this importance tends to increase as the amount of data grows. It is one of the main analytical methods in data mining and its method influences its results directly. K-means is a typical clustering algorithm[3]. It mainly consists of two phases i.e. initializing random clusters and to find the nearest neighbour. ...

We propose a method of using clustering techniques to partition a set of orders. We define the term order as a sequence of objects that are sorted according to some property, such as size, preference, or price. These orders are useful for, say, carrying out a sensory survey. We propose a method called the ko’means method, which is a modified version of a k-means method, adjusted to handle order...

This study explored the relationship between students’ math ability and effort in predicting 6 grade students’ performance in the Assessment and LEarning in Knowledge Spaces (ALEKS) system. The students were clustered into four groups by Kmeans: high ability high effort, high ability low effort, low ability high effort and low ability low effort. A one-way ANOVA indicated that student’s math po...

This paper presents a research on clustering an image collection using multi-visual features. The proposed method extracted a set of visual features from each image and performed multi-dimensional K-Means clustering on the whole collection. Furthermore, this work experiments on different number of visual features combination for clustering. 2, 3, 5 and 7 pair of visual features chosen from a to...

The problem of determining “the right number of clusters” in K-Means has attracted considerable interest, especially in the recent years. However, to the authors’ knowledge, no experimental results of their comparison have been reported so far. This paper intends to present some results of such a comparison involving eight cluster selection options that represent four different approaches. The ...