Résultats de recherche pour « Elbow Method »

Clustering is a data mining technique used to analyse data that has variations and the number of lots. Clustering was process of grouping data into a cluster, so they contained data that is as similar as possible and different from other cluster objects. SMEs Indonesia has a variety of customers, but SMEs do not have the mapping of these customers so they did not know which customers are loyal or otherwise. Customer mapping is a grouping of customer profiling to facilitate analysis and policy of SMEs in the production of goods, especially batik sales. Researchers will use a combination of K-Means method with elbow to improve efficient and effective k-means performance in processing large amounts of data. K-Means Clustering is a localized optimization method that is sensitive to the selection of the starting position from the midpoint of the cluster. So choosing the starting position from the midpoint of a bad cluster will result in K-Means Clustering algorithm resulting in high errors and poor cluster results. The K-means algorithm has problems in determining the best number of clusters. So Elbow looks for the best number of clusters on the K-means method. Based on the results obtained from the process in determining the best number of clusters with elbow method can produce the same number of clusters K on the amount of different data. The result of determining the best number of clusters with elbow method will be the default for characteristic process based on case study. Measurement of k-means value of k-means has resulted in the best clusters based on SSE values on 500 clusters of batik visitors. The result shows the cluster has a sharp decrease is at K = 3, so K as the cut-off point as the best cluster.

Abstract K-Means is a simple clustering algorithm that has the ability to throw large amounts of data, partition datasets into several clusters k. The algorithm is quite easy to implement and run, relatively fast and efficient. Another division of K-Means still has several weaknesses, namely in determining the number of clusters, determining the cluster center. The results of the cluster formed from the K-means method is very dependent on the initiation of the initial cluster center value provided. This causes the results of the cluster to be a solution that is locally optimal. This research was conducted to overcome the weaknesses in the K-Means algorithm, namely: improvements to the K-Means algorithm produce better clusters, namely the application of Sum Of Squared Error (SSE) to help K-Means Clustering in determining the optimum number of clusters, From this modification process, it is expected that the cluster center obtained will produce clusters, where the cluster members have a high level of similarity. Improving the performance of the K-Means cluster will be applied to determining the number of clusters using the elbow method.

Several algorithms are applied in the literature of clustering such as K-means, Fuzzy C-Means, and Single Linkage, etc. K-means Algorithm is the most commonly used because of its simplicity. How ever, the best number of clusters in K-means possesses a weakness. This study was carried out in determin

K-means clustering is an unsupervised clustering method that requires an initial decision of number of clusters. One method to determine the number of clusters is the elbow method, a heuristic method that relies on a graphical plot of the within-cluster sum of squares against the number of clusters. The method uses the number based on the elbow point, the point closest to $90^\circ$ that indicates the most optimum number of clusters. This research improves the elbow method such that the selection of cluster numbers is unbiased towards visual interpretation. We use the analytical geometric formula to calculate an angle between lines and real analysis principle of derivative to simplify the elbow point determination. We also consider every possibility of the elbow method graph behaviour such that the algorithm is universally applicable. The result is that the elbow point can be measured precisely with a simple algorithm that does not involve complex functions or calculations. This improved method gives an alternative of more reliable cluster determination method that contributes to more optimum k-means clustering, obtained by the fewest clusters with the lowest error.

The most widely used algorithm in the cluster partitioning method is the K-Means algorithm, K-Means is an iteration algorithm with the user determining the number of clusters that need to be grouped and determining the centroid for each cluster so that the level of similarity between members in one group is high while the level of similarity with members in other groups is very low.Historically K-Means is still the best grouping algorithm among other grouping algorithms with the ability to group a number of data with relatively fast and efficient computing time.The K-Means algorithm is widely implemented in various fields in industrial and scientific applications and is very suitable for processing quantitative data with numeric attributes, but there are still weaknesses in this algorithm.Weaknesses of the K-Means algorithm include determining the number of clusters based on assumptions and relying heavily on the initial selection of centroids to overcome this weakness, in this study, we propose the use of the elbow method to determine the best number of clusters and initials.Centroid determination based on average and median data.The results of this study indicate that using initial cluster center determination based on average data makes the number of iterations needed to achieve uniformity in clusters 22.58% less than initial random cluster determination and determining the best number of clusters using the elbow method makes the required iteration 25% less than using the number of other clusters.

Prostate cancer disease is one of the common types that cause men's prostate damage all over the world. Prostate-specific membrane antigen (PSMA) expressed by type-II is an extremely attractive style for imaging-based diagnosis of prostate cancer. Clinically, photodynamic therapy (PDT) is used as noninvasive therapy in treatment of several cancers and some other diseases. This paper aims to segment or cluster and analyze pixels of histological and near-infrared (NIR) prostate cancer images acquired by PSMA-targeting PDT low weight molecular agents. Such agents can provide image guidance to resection of the prostate tumors and permit for the subsequent PDT in order to remove remaining or noneradicable cancer cells. The color prostate image segmentation is accomplished using an optimized image segmentation approach. The optimized approach combines the k-means clustering algorithm with elbow method that can give better clustering of pixels through automatically determining the best number of clusters. Clusters' statistics and ratio results of pixels in the segmented images show the applicability of the proposed approach for giving the optimum number of clusters for prostate cancer analysis and diagnosis.

This research was conducted to find the groups of elementary schools in the Special Capital Region of Jakarta, also known as DKI Jakarta. Elementary school data were selected because it is the first stage of formal education in Indonesia. This research used K-means clustering with the elbow method to determine optimal cluster numbers. The optimal cluster number is three with Cluster 2 having the most members, followed by Cluster 1 and Cluster 0. The data distribution of Cluster 2 shows that the second-most student body and public schools located in East and West Jakarta have an adequate student-to-teacher ratio based on Article 17 of Government Regulation 74, 2008.

This study aims to obtain cluster data of potential customers using the K-Means clustering approach supported by the elbow method to determine the correct number of clusters. The data sample that was processed was 100 customer data from a minimarket containing three criteria (gender, age, and purchase retention). The number of initial clusters is determined as 5 and then processed by calculating K-Means. The calculation of the SSE value in the K-Means process produces the lowest SSE value, and the sharpest elbow angle graph visualization is in cluster 4. So, it can be stated that the best number of clusters in this K-Means calculation is four (4) which are used as material for further analysis. Then the analysis results of four (4) clusters state that potential customers are those with high purchase retention, consisting of female customers who dominate in the three (3) clusters. Most potential female customers are customers with an age range above 35 years. Meanwhile, customers with less potential are spread across each cluster with varied gender and age but are not dominant. Thus, this knowledge can be used as a consideration for the management in determining the right promotion strategy.

Stroke is the second most deadly disease in the world according to WHO. The sufferer has an injury to the nervous system. Because of this, health experts, especially in the field of nursing, need special attention. Technological advances continue to change over time so that information needs are needed in life. Currently the data on stroke sufferers is extensive enough so that adequate information presentation techniques are needed so that the information received is very accurate and in accordance with user needs. Therefore, it is necessary to process data mining on stroke patient data to obtain useful information for users. This study aims to prove the performance of the Elbow Method to produce the optimum k value in the stroke prediction data using the K-Nearest Neighbors (KNN) algorithm. The optimum k value is generated from the Elbow Method which is executed with the Google Collaboratory using the Python programming language. The test results show that the Elbow Method produces the optimum k value at k = 7. The KNN model that uses the optimum k value from the Elbow Method can increase the accuracy and precision values ​​reaching 6% and 0.12, respectively.