Poverty is one of the problems that must be faced by developing countries, including Indonesia and especially province West Java. This problem exacerbated Covid-19 pandemic. can also have other consequences, such as increased crime death. To facilitate government programs support, it necessary to group cities/districts according poverty level. The analysis was carried out using K-Means Fuzzy C-...

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...

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are conside...

Variable string length genetic algorithm (GA) is used for developing a novel nonparametric clustering technique when the number of clusters is not fixed a priori. Chromosomes in the same population may now have different lengths since they encode different number of clusters. The crossover operator is redefined to tackle the concept of variable string length. Cluster validity index is used as a...

The Affinity Propagation (AP) is a clustering algorithm that does not require pre-set K cluster numbers. We improve the original AP to Map/Reduce Affinity Propagation (MRAP) implemented in Hadoop, a distribute cloud environment. The architecture of MRAP is divided to multiple mappers and one reducer in Hadoop. In the experiments, we compare the clustering result of the proposed MRAP with the K-...

Abstract: Predicting barotrauma occurrence in intensive care patients is a difficult task. Data Mining modelling can contribute significantly to the identification of patients who will suffer barotrauma. This can be achieved by grouping patient data, considering a set of variables collected from ventilators directly related with barotrauma, and identifying similarities among them. For clusterin...

A study of VMware ESXi 5.1 server has been carried out to find the optimal set of parameters which suggest usage of different resources of the server. Feature selection algorithms have been used to extract the optimum set of parameters of the data obtained from VMware ESXi 5.1 server using esxtop command. Multiple virtual machines (VMs) are running in the mentioned server. K-means algorithm is ...

This study investigates an improved k-means clustering algorithm for segregating large geospatial data. Although the conventional k-means method is sufficient for datasets with minimal data, it does not perform well and, therefore yields poor accuracy for high-volume datasets. Clustering methods are one of the most important components in data classification, visualization, and mining highvolum...

This study aims to examine the differences in various cluster validity indexes grouping of credit customers at Bank X Malang City, Indonesia using average linkage and Euclidean distance methods. uses primary data with variables used are service quality, environment, mode, willingness pay, obedient paying behavior obtained through a questionnaire Likert scale purposive sampling distributed 100 r...

Surface electromyographic (sEMG) activity of the biceps muscle was recorded from 13 subjects. Data was recorded while subjects performed dynamic contraction until fatigue and the signals were segmented into two parts (Non-Fatigue and Fatigue). An evolutionary algorithm was used to determine the elbow angles that best separate (using Davies-Bouldin Index, DBI) both Non-Fatigue and Fatigue segmen...