Open AccessBigdata Clustering using X-means method with Euclidean Distance
Author(s) -
Niskarto Zendrato,
Hanna Willa Dhany,
Novriadi Antonius Siagian,
Fahmi Izhari
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1566/1/012103
Subject(s) - centroid , euclidean distance , cluster analysis , similarity (geometry) , k medians clustering , cluster (spacecraft) , complete linkage clustering , point (geometry) , data point , mathematics , data mining , computer science , range (aeronautics) , algorithm , pattern recognition (psychology) , artificial intelligence , statistics , correlation clustering , cure data clustering algorithm , geometry , image (mathematics) , engineering , programming language , aerospace engineering
Centroid is the central point of data in the grouping process, it is necessary to analyze the centroid in determining the initial value in the initial clustering process. So it is used as a cluster center point in the X-Means algorithm clustering process. Determine cluster center points or centroid, measure the performance of the X-Means algorithm with range cluster parameters by measuring distances between centroid for a fast and efficient way to group unstructured data, and to speed up the model construction process and divide several centroid in half to match the data as a test tool for the analysis of the X-Means method. From testing using the X-Means algorithm with the determination of the number of Centroid clusters carried out by modifying the X-Means method to do some determination of the centroid to get the results of 11 iterations. From the results of these tests produce good cluster members the level of similarity of data with other data and in determining the number of clusters, using the modification of the Euclidean distance method, get better results of the similarity level of each member compared to randomly determining the number of clusters with several iterations.