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Nearest-neighbour-induced isolation similarity and its impact on density-based clustering

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Version 2 2024-06-04, 12:11
Version 1 2019-10-09, 13:17
conference contribution
posted on 2024-06-04, 12:11 authored by Xiaoyu Qin, Kai Ming Ting, Ye ZhuYe Zhu, Vincent CS Lee
A recent proposal of data dependent similarity called Isolation Kernel/Similarity has enabled SVM to produce better classification accuracy. We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead. We formally prove the characteristic of Isolation Similarity with the use of the proposed method. The impact of Isolation Similarity on densitybased clustering is studied here. We show for the first time that the clustering performance of the classic density-based clustering algorithm DBSCAN can be significantly uplifted to surpass that of the recent density-peak clustering algorithm DP. This is achieved by simply replacing the distance measure with the proposed nearest-neighbour-induced Isolation Similarity in DBSCAN, leaving the rest of the procedure unchanged. A new type of clusters called mass-connected clusters is formally defined. We show that DBSCAN, which detects density-connected clusters, becomes one which detects mass-connected clusters, when the distance measure is replaced with the proposed similarity. We also provide the condition under which mass-connected clusters can be detected, while density-connected clusters cannot.

History

Volume

33

Pagination

4755-4762

Location

Honolulu, Hawaii

Open access

  • Yes

Start date

2019-01-27

End date

2019-02-01

eISSN

2374-3468

ISBN-13

9781577358091

Language

eng

Publication classification

E1 Full written paper - refereed

Title of proceedings

Proceedings of the Combined Conferences : 33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence

Event

AAAI Conference on Artificial Intelligence., Innovative Applications of Artificial Intelligence Conference and AAAI Symposium on Educational Advances in Artificial Intelligence. Combined Conference (2019 : 33rd, 31st & 9th : Honolulu, Hawaii)

Issue

1

Publisher

AAAI Press

Place of publication

[Honolulu, Hawaii]