Analysis of Representative Values in Clustering using the CURE Algorithm
Umaya Ramadhani Putri Nst1, Sutarman2, Pahala Sirait3
1Umaya Ramadhani Putri Nst, Bachelor of Information Technology, Universitas Sumatera Utara, Indonesia.
2Sutarman, Magister of Applied Probability and Statistics, Northern Illionis University, Amerika.
3Pahala Sirait, Magister of Computer Science, Universitas Indonesia, Indonesia.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 627-630 | Volume-9 Issue-2, July 2020. | Retrieval Number: B3742079220/2020©BEIESP | DOI: 10.35940/ijrte.B3742.079220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The collection of data that everyone has on earth has a fully agreed upon value of knowledge. Analysis of a collection of data that can accommodate a long processing time, for this we need an algorithm that can provide a comparison of the acceleration of the analysis process. One process of data analysis is clustering, which is a process of grouping large amounts of data so that it is easy to understand. One of the algorithms in the clustering process is CURE (Clustering Using Representative) where CURE random sample-based data bases partition the data using representative points called representative points. Sample-based process will provide better processing time acceleration because it will only be done on the data collection, not the whole data. This representative point determines the processing time of the testing carried out in the input. Values, representative values, and shrinkage values will provide a faster settlement process for the values inputted according to the correct conditions.
Keywords: Representative, CURE algorithm.