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 ...

A novel method of performing multiscale segmentation of radar reflectivity data using statistical properties within the radar data itself is introduced. The method utilizes a K-Means clustering of texture vectors computed within the

The most widely used partitioning method in cluster analysis is the k-means clustering which minimizes within-cluster sum of squares. However, the k-means clustering is sensitive to outliers or cluster structures. We introduce the k-spatial medians clustering which is less sensitive to outliers as an alternative to the k-means clustering and compare two clustering methods for some arti cial dat...

A new clustering algorithm based on grid projections is proposed. This algorithm, called U*C, uses distance information together with density structures. The number of clusters is determined automatically. The validity of the clusters found can be judged by the U*-Matrix visualization on top of the grid. A U*-Matrix gives a combined visualization of distance and density structures of a high dim...

k-means is the basic method applied in many data clustering problems. As is known, its natural modification can be applied to projection clustering by changing the cost function from the squared-distance from the point to the squared distance from the affine subspace. However, to apply thus approach we need the beforehand knowledge of the dimension. In this paper we show how to modify this appr...