The general election is a democratic process that carried out in every country whose system of government presidential, including Indonesia, which conducts it five years. In fact, some people abstain, leading to budget wasting and missing target. Thus, very important identify clusters districts map the number voters for upcoming election. This needs prediction help reduce budgeting risk as an e...

This work presents an implementation of a parallel Fuzzy c-means cluster analysis tool, which implements both aspects of cluster investigation: the calculation of clusters’ centers with the degrees of membership of records to clusters, and the determination of the optimal number of clusters for a given dataset using the PBM index. Topics of Interest: Unsupervised Classification, Fuzzy c-Means, ...

Introduction: The Coronavirus has crossed geographical borders. This study was performed to rank and cluster Iranian provinces based on coronavirus disease (COVID-19) recorded cases from February 19 to March 22, 2020. Materials and Methods: This cross-sectional study was conducted in 31 provinces of Iran using the daily number of confirmed cases. Cumulative Frequency (CF) and Adjusted CF (ACF)...

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...