Open AccessDbscan Assisted by Hybrid Genetic K Means Algorithm
Author(s) -
K. V. Suresh,
Mrs. K. Chandusha
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f8061.038620
Subject(s) - dbscan , cluster analysis , computer science , data mining , partition (number theory) , algorithm , genetic algorithm , quadratic equation , artificial intelligence , machine learning , cure data clustering algorithm , correlation clustering , mathematics , geometry , combinatorics
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.