Open AccessNI-DBSCAN: DBSCAN under Non-IID
Author(s) -
Yikun Lv,
He Jiang,
Pinchen Pan
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 - 10.1088/1742-6596/1533/2/022110
Subject(s) - dbscan , cluster analysis , categorical variable , pattern recognition (psychology) , similarity (geometry) , computer science , noise (video) , data mining , artificial intelligence , cure data clustering algorithm , mathematics , correlation clustering , machine learning , image (mathematics)
DBSCAN (Density Based Spatial Clustering of Application with Noise) is an example of density-based clustering algorithm. Aiming at problem that DBSCAN algorithm assumes that the data are independent and identically distributed and the traditional distance formula is difficult to accurately calculate the similarity degree between categorical data. Density Based Spatial clustering algorithm of Application with Noise under Non-IID (NI-DBSCAN) is proposed. The unsupervised clustering problem of categorical data is dealt with by means of the Non-IID (non-independent and identical distribution) thought. Using coupling similarity to measure similarity can better reflect the “real relationship” between categorical data. The experimental results on the UCI dataset show that the algorithm can obtain satisfactory clustering results and improve the applicability and accuracy of the algorithm.