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 ...

Clustering is one of the important data mining techniques. k-Means [1] is one of the most important algorithm for Clustering. Traditional k-Means algorithm selects initial centroids randomly and in k-Means algorithm result of clustering highly depends on selection of initial centroids. k-Means algorithm is sensitive to initial centroids so proper selection of initial centroids is necessary. Thi...

The k-means algorithm with cosine similarity, also known as the spherical k-means algorithm, is a popular method for clustering document collections. However, spherical k-means can often yield qualitatively poor results, especially for small clusters, say 25-30 documents per cluster, where it tends to get stuck at a local maximum far away from the optimal. In this paper, we present the first-va...

Clustering is the unsupervised classification of patterns (data items, feature vectors, or observations) into groups (clusters). Clustering in data mining is very useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based similarity measure in order to partition the database such that data points in the same partition are more s...

In this paper, we propose a new document clustering method based on the K-means method (kmeans). In our method, we allow only finite candidate vectors to be representative vectors of kmeans. We also propose a method for constructing these candidate vectors using documents that have the same word. We participated in NTCIR-4 WEB Task D (Topic Classification Task) and experimentally compared our m...

This study presents a position model for evaluating the image of tourists a destination. The evaluation is based on secondary data from 1999 through 2004, using a database composed of 20,023 respondents. Data are analyzed using the K-Means data mining method. Analytical results indicate that the destination image position (DIP) model is established, and four groups of visitor are identified. Th...

Clustering, in particular k-means clustering, is a central topic in data analysis. Clustering with Bregman divergences is a recently proposed generalization of k-means clustering which has already been widely used in applications. In this paper we analyze theoretical properties of Bregman clustering when the number of the clusters k is large. We establish quantization rates and describe the lim...