Implementation Of K-means Algorithm Research Articles

Implementation of K-Means Algorithm with Clustering Method to Data Management of Covid-19 Case in West Bandung Using Orange Application

Covid-19 cases in Indonesia have not disappeared to the until now. Coronavirus has spread to almost all provinces in Indonesia, one of the province is West Java. There are 27 regencies/cities in West Java that have become the spread of the Covid-19 case, one of which is West Bandung Regency. To make it easier for the regional government of West Bandung Regency to take action in an effort to prevent the spread of Covid-19, research is needed to determine the level of distribution of Covid-19 cases which are divided into 3 clusters, namely high, medium and low clusters. Researchers analyzed using the data mining method with the K-Means Clustering algorithm through the Orange Application. Data processing is started from determining amount of the data to be clustered into 3 high, medium and low, then determining the initial centroid cluster center point (iteration 1) that has been determined randomly, then calculating the distance of each data to the center of the cluster between objects to closest centroid. The distribution of cluster 1, cluster 2, and cluster 3 as many as 16 data in the K-Means Clustering process using the Orange Application. The research results show that there are 8 sub-districts for high clusters, 2 sub-districts for medium clusters and 6 sub-districts for low clusters in West Bandung Regency.

Implementation of K-means algorithm in data analysis

Some large companies have difficulty in providing products even though the products are still available in the warehouse. Based on these problems, a solution is needed in managing cosmetic products and can find the right strategy so that it can increase business in the field of sales and improve sales services by using algorithms in data mining that can overcome these problems, such as clustering techniques that use the K-means clustering algorithm as a way to measure proximity data between cosmetic products based on transactions that have occurred. The specialty of the analysis of the management of cosmetic products in this study is that it produces data on products that are not sold enough so that it can provide prevention so that the accumulation of these products does not occur. The use of K-means clustering also makes it easier to collect cosmetic sales transaction data, can solve problems in classifying cosmetic product sales transaction data and find out which products should be in cosmetics stock so as to increase sales profits

Implementation of K-Means Algorithm and Dynamic Routing Protocol in VANET

With the growth of Vehicular Ad-hoc Networks, many services delivery is gaining more attention from the intelligent transportation system. However, mobility characteristics of vehicular networks cause frequent disconnection of routes, especially during the delivery of data. In both developed and developing countries, a lot of time is consumed due to traffic congestion. This has significant negative consequences, including driver stress due to increased time demand, decreased productivity for various personalized and commercial vehicles, and increased emissions of hazardous gases especially air polluting gases are impacting public health in highly populated areas. Clustering is one of the most powerful strategies for achieving a consistent topological structure. Two algorithms are presented in this research work. First, a k-means clustering algorithm in which dynamic grouping by k-implies is performed that fits well with Vehicular network’s dynamic topology characteristics. The suggested clustering reduces overhead and traffic management. Second, for inter and intra-clustering routing, the dynamic routing protocol is proposed, which increases the overall Packet Delivery Ratio and decreases the End-to-End latency. Relative to the cluster-based approach, the proposed protocol achieves improved efficiency in terms of Throughput, Packet Delivery Ratio, and End-to-End delay parameters comparing the situations by taking different number of vehicular nodes in the network.

Parallel and fault-tolerant k-means clustering based on the actor model

K-means algorithm is a well-known unsupervised machine learning tool that aims at splitting a given dataset into a fixed number of clusters via iterative refinement approach. Running such an algorithm on today’s datasets that are characterized by its high multidimensionality and huge size requires using fault-tolerance mechanisms to mitigate the impact of possible failures. In this paper, we propose an actor-based implementation of k-means algorithm. The algorithm was made fault-tolerant by periodically saving the centroids into a stable storage during the failure-free execution, and restarting from the last saved centroids upon a failure. This was implemented in two different ways: optimistic checkpointing (blocking) and pessimistic checkpointing (non-blocking). The actor-based k-means algorithm was evaluated on a machine with eight cores. The experiments showed that the proposed algorithm scales very well as the number of workers increases, and can be up to ∼ 2x faster than a Java-thread-based implementation of k-means algorithm. The results also showed that the optimistic algorithm outperformed the pessimistic one, specifically, in the presence of competing I/O operations. Several failures were forced to occur during the execution to evaluate the performance of the fault-tolerant implementations. The experiments showed that the average amount of lost work ranged from 3–6%.

Implementation of K-Means Algorithm using Clustering Rules on Medical Data Sets

During the process of mining frequent item sets, when minimum support is little, the production of candidate sets is a kind of time-consuming and frequent operation in the mining algorithm. The K-Means algorithm does not need to produce the candidate sets, the database which provides the frequent item set is compressed to a frequent pattern tree (or FP tree), and frequent item set is mining by using of FP tree. These algorithms considered as efficient because of their compact structure and also for less generation of candidates itemsets compare to Apriori and Apriori like algorithms. Therefore this paper aims to presents a basic Concepts of some of the algorithms (K-Means Algorithmn, COFI-Tree, CT-PRO) based upon the FP- Tree like structure for mining the frequent item sets along with their capabilities and comparisons. Data mining implementation on spatial data to generate rules and patterns using Frequent Pattern (FP)-Growth algorithm is the major concern of this research study. We presented in this paper how data mining can apply on spatial data.

The Implementation of K-Means Algorithm as Image Segmenting Method in Identifying the Citrus Leaves Disease

The purpose of this study is to identify the disease on citrus leaves. A digital imagery makes it possible to identify disease automatically. Three diseases to be identified in this research are CVPD (Citrus Vein Phloem Degeneration), Downy Mildew, and Cendawan Jelaga. The research will study the implementation of an image segmentation to analyze the citrus leaves diseases. The method that will be used to do image segmentation is K-Means. The segmentation which will be carried out consist of two kinds, namely a leaf segmentation and a disease segmentation. After segmentation process, the results of disease segmentation are classified by using the K-Nearest Neighbor (K-NN) algorithm to know its disease class. From data analysis, the results of the optimal cluster shows that the leaf segmentation consist of 2 clusters and the disease segmentation consist of 9 clusters. While the obtained optimal parameter K gives score of 4. The accuracy percentage for disease identification in this study is 90.83%. Furthermore, the analysis states that the accuracy can be more increased by using a minimum bound parameters. Finally, overall the results show the optimal value at the minimum bound of 3%, its accuracy can be increased to 99.17%.

Pixel Value Graphical Password Scheme: Compatibility of K-means Clustering Algorithm as Pixel Value Password Fault Tolerance Mechanism

In September 2018, the patent for pixel value graphical password scheme was granted in Malaysia. The graphical password scheme was designed to reduce the complexity of previously developed graphical password scheme where a user only requires to load their personal image as password instead of complex graphical challenge during authentication. As the guardian of digital access, Pixel Value Access Control was highly invincible from password pixel forgery attack where a little bit different pixel value derived from loaded image will deny the access. Only the original enrolled image from a registered user can be recognized by Pixel Value Access Control to authenticate the respective username. That fact makes the graphical password scheme is a trusted access control mechanism but, on the other hand, it makes users bound with the only original password pixel image file. Thus, Pixel Value Access Control need to be installed the pixel value fault tolerance mechanism where it could allow users to acquire their password pixel image file from various storage media. The clustering technique was suggested to solve this issue where it allows an altered pixel password being recognized as the same group of the original pixel password. There are number of clustering algorithms developed for various purposed and application of digital image clustering. K-Means algorithm is one the partition-based clustering algorithm that found to be the simplest and fastest clustering algorithm as suggested by many researchers. This paper is mainly to exhibit the selection of K-Means clustering algorithm became the crucial algorithm for Pixel Value Access Control password pixel fault tolerance algorithm. Background of this topic was briefly explained in introduction section, the implementation of K-Means algorithm as Pixel Value Access Control fault tolerance was elaborate in section 2 and followed by validation of the implementation in section 3. At the end of this paper, there is conclusion for this study and followed by list of references.

The implementation of two stages clustering (k-means clustering and adaptive neuro fuzzy inference system) for prediction of medicine need based on medical data

Medication planning aim to get types, amount of medicine according to needs, and avoid the emptiness medicine based on patterns of disease. In making the medicine planning is still rely on ability and leadership experience, this is due to take a long time, skill, difficult to obtain a definite disease data, need a good record keeping and reporting, and the dependence of the budget resulted in planning is not going well, and lead to frequent lack and excess of medicines. In this research, we propose Adaptive Neuro Fuzzy Inference System (ANFIS) method to predict medication needs in 2016 and 2017 based on medical data in 2015 and 2016 from two source of hospital. The framework of analysis using two approaches. The first phase is implementing ANFIS to a data source, while the second approach we keep using ANFIS, but after the process of clustering from K-Means algorithm, both approaches are calculated values of Root Mean Square Error (RMSE) for training and testing. From the testing result, the proposed method with better prediction rates based on the evaluation analysis of quantitative and qualitative compared with existing systems, however the implementation of K-Means Algorithm against ANFIS have an effect on the timing of the training process and provide a classification accuracy significantly better without clustering.

Newton Algorithms for Riemannian Distance Related Problems on Connected Locally Symmetric Manifolds

The squared distance function is one of the standard functions on which an optimization algorithm is commonly run, whether it is used directly or chained with other functions. Illustrative examples include center of mass computation, implementation of k-means algorithm and robot positioning. This function can have a simple expression (as in the Euclidean case), or it might not even have a closed form expression. Nonetheless, when used in an optimization problem formulated on non-Euclidean manifolds, the appropriate (intrinsic) version must be used and depending on the algorithm, its gradient and/or Hessian must be computed. For many commonly used manifolds a way to compute the intrinsic distance is available as well as its gradient, the Hessian however is usually a much more involved process, rendering Newton methods unusable on many standard manifolds. This article presents a way of computing the Hessian on connected locally-symmetric spaces on which standard Riemannian operations are known (exponential map, logarithm map and curvature). Although not a requirement for the result, describing the manifold as naturally reductive homogeneous spaces, a special class of manifolds, provides a way of computing these functions. The main example focused in this article is centroid computation of a finite constellation of points on connected locally symmetric manifolds since it is directly formulated as an intrinsic squared distance optimization problem. Simulation results shown here confirm the quadratic convergence rate of a Newton algorithm on commonly used manifolds such as the sphere, special orthogonal group, special Euclidean group, symmetric positive definite matrices, Grassmann manifold and projective space.

K-Means Walk: Unveiling Operational Mechanism of a Popular Clustering Approach for Microarray Data

Since data analysis using technical computational model has profound influence on interpretation of the final results, basic understanding of the underlying model surrounding such computational tools is required for optimal experimental design by target users of such tools. Despite wide variation of techniques associated with clustering, cluster analysis has become a generic name in bioinformatics and is seen to discover the natural grouping(s) of a set of patterns, points or sequences. The aim of this paper is to analyze k-means by applying a step-by-step k-means walk approach using graphic-guided analysis to provide clear understanding of the operational mechanism of the k-means algorithm. Scattered graph was created using theoretical microarray gene expression data which is a simplified view of a typical microarray experiment data. We designate the centroid as the first three initial data points and applied Euclidean distance metrics in the k-means algorithm leading to assignment of these three data points as reference point to each cluster formation. A test is conducted to determine if there is a shift in centroid before the next iteration is attained. We were able to trace out those data points in same cluster after convergence. We observed that, as both the dimension of data and gene list increases for hybridization matrix of microarray data, computational implementation of k-means algorithm becomes more rigorous. Furthermore, the understanding of this approach will stimulate new ideas for further development and improvement of the k-means clustering algorithm especially within the confines of the biology of diseases and beyond. However, the major advantage will be to give improved cluster output for the interpretation of microarray experimental results, facilitate better understanding for bioinformaticians and algorithm experts to tweak k-means algorithm for improved run-time of clustering.

A Modified Fastmap K-Means Clustering Algorithm for Large Scale Gene Expression Datasets

Clustering is an important tool of data mining to extract the context or meaningful patterns from the datasets. To extract the patterns from gene expression datasets, cluster analysis is used. From last two to three years, there are noticeably increase in public datasets, like geo, arrayexpress, genebank, are noted. All these increase in public datasets are not only in numbers but also in dimensions. To analyze single experiment, 1000's of genes and 100's of samples are available. To handle these large dimensions, we have to moderate or modify the traditional clustering algorithms. K-means clustering algorithm is one of the most used and tested clustering algorithm not only for gene expression datasets but also for various different datasets. But, as the dimension goes on increasing, CPU time requirement and memory requirement also increasing. Here, we try to increase the speed of K-means algorithm by adding additional phase (by using moderate FastMap) before implementation of k-means algorithm on the datasets. So, Modified FastMap K-means Clustering Algorithm, is a two phase algorithm, which try to reduced CPU time and memory requirements as compared to tradition K-means requirements. We have shown tabular results for three datasets, which are downloaded from public repository, NCBI, geo. The algorithm can successfully generate good results for large as well as small datasets.