Dbscan Assisted by Hybrid Genetic K Means Algorithm
Suresh Kurumalla1, Chandusha Kanda2
1Dr. K. Suresh, Assistant professor in Anil Neerukonda Institute of Technology & Sciences, Visakhapatnam.
2Mrs. K. Chandusha, Assistant professor in Anil Neerukonda institute of Technology & Sciences, Visakhapatnam.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 1973-1979 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8061038620/2020©BEIESP | DOI: 10.35940/ijrte.F8061.038620
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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 data mining algorithms functioning is main concern, when the data becomes to a greater extent. Clustering analysis is a active and dispute research direction in the region of data mining for complex data samples. DBSCAN is a density-based clustering algorithm with several advantages in numerous applications. However, DBSCAN has quadratic time complexity i.e. making it complicated for realistic applications particularly with huge complex data samples. Therefore, this paper recommended a hybrid approach to reduce the time complexity by exploring the core properties of the DBSCAN in the initial stage using genetic based K-means partition algorithm. The technological experiments showed that the proposed hybrid approach obtains competitive results when compared with the usual approach and drastically improves the computational time.
Keywords: DBSCAN, Genetic Algorithm, K-Means Algorithm, Image Database, Clustering.
Scope of the Article: Algorithm Engineering.