Does K-Means reasonably divides the data into k groups is an important question that arises when one works on Image Segmentation? Which color space one should choose and how to ascertain that the k we determine is valid? The purpose of this study was to explore the answers to aforementioned questions. We perform K-Means on a number of 2-cluster, 3cluster and k-cluster color images (k>3) in RGB ...

Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique are dependent on the initialization of cluster centers. In this article we propose a new algorithm to initialize the clusters. The proposed algorithm is based on finding a set of medians extracted from a dimension with maximu...

This paper describes a new approach to the segmentation of textured gray-scale images based on image pre-"ltering and fractal features. Traditionally, "lter bank decomposition methods consider the energy in each band as the textural feature, a parameter that is highly dependent on image intensity. In this paper, we use fractal-based features which depend more on textural characteristics and not...

Clustering is a basic process in data analysis. It aims to partition a set of objects into groups called clusters such that, ideally, objects in the same group are similar and objects in different groups are dissimilar to each other. There are many scenarios where such a partition is useful. It may, for example, be used to structure the data to allow efficient information retrieval, to reduce t...

Data mining is a method that is used to select the information from large datasets and it performs the principal task of data analysis. The Clustering is a technique that consist groups of data and elements into disjoined clusters of data. The same cluster data are related to similar cluster and different cluster data belong to different cluster. Clustering can be done different methods like pa...

This paper implements a tool, referred to as the Automated Group Decomposition Program (AGDP), which divides a class of students into groups, using the k-means algorithm, for the purpose of collaborative learning, and then heterogenizes the groups based on a factor called the degree of heterogeneity (DOH). The tool takes as input two sets of scores and the students’ roll numbers and outputs the...

We build a general and highly applicable clustering theory, which we call cross-entropy clustering (shortly CEC) which joins advantages of classical kmeans (easy implementation and speed) with those of EM (affine invariance and ability to adapt to clusters of desired shapes). Moreover, contrary to k-means and EM, CEC finds the optimal number of clusters by automatically removing groups which ca...

This study proposes an innovative measure for evaluating the performance of text clustering. In using K-means algorithm and Kohonen Networks for text clustering, the number clusters is fixed initially by configuring it as their parameter, while in using single pass algorithm for text clustering, the number of clusters is not predictable. Using labeled documents, the result of text clustering us...

K-means is an effective clustering technique used to separate similar data into groups based on initial centroids of clusters. In this paper, Normalization based K-means clustering algorithm(N-K means) is proposed. Proposed N-K means clustering algorithm applies normalization prior to clustering on the available data as well as the proposed approach calculates initial centroids based on weights...