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Ros F, Riad R, Guillaume S. PDBI: a Partitioning Davies-Bouldin Index for Clustering Evaluation. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.01.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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Rojas‐Thomas JC, Santos M. New internal clustering validation measure for contiguous arbitrary‐shape clusters. INT J INTELL SYST 2021. [DOI: 10.1002/int.22521] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
| | - Matilde Santos
- Institute of Knowledge Technology University Complutense of Madrid Madrid Spain
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Azhir E, Jafari Navimipour N, Hosseinzadeh M, Sharifi A, Darwesh A. An automatic clustering technique for query plan recommendation. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.09.037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Liu Y, Jiang Y, Hou T, Liu F. A new robust fuzzy clustering validity index for imbalanced data sets. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.08.041] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Abstract
The existing density clustering algorithms have high error rates on processing data sets with mixed density clusters. For overcoming shortcomings of these algorithms, a double-density clustering method based on Nearest-to-First-in strategy, DDNFC, is proposed, which calculates two densities for each point by using its reverse k nearest neighborhood and local spatial position deviation, respectively. Points whose densities are both greater than respective average densities of all points are core. By searching the strongly connected subgraph in the graph constructed by the core objects, the data set is clustered initially. Then each non-core object is classified to its nearest cluster by using a strategy dubbed as ‘Nearest-to-First-in’: the distance of each unclassified point to its nearest cluster calculated firstly; only the points with the minimum distance are placed to their nearest cluster; this procedure is repeated until all unclassified points are clustered or the minimum distance is infinite. To test the proposed method, experiments on several artificial and real-world data sets are carried out. The results show that DDNFC is superior to the state-of-art methods like DBSCAN, DPC, RNN-DBSCAN, and so on.
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