Abstract
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 density-based 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.
Original language | English |
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Title of host publication | Proceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence |
Editors | Pascal Van Hentenryck, Zhi-Hua Zhou |
Place of Publication | Palo Alto CA USA |
Publisher | Association for the Advancement of Artificial Intelligence (AAAI) |
Pages | 4755-4762 |
Number of pages | 8 |
ISBN (Electronic) | 9781577358091 |
DOIs | |
Publication status | Published - 2019 |
Event | AAAI Conference on Artificial Intelligence 2019 - Honolulu, United States of America Duration: 27 Jan 2019 → 1 Feb 2019 Conference number: 33rd https://aaai.org/Conferences/AAAI-19/ |
Publication series
Name | Proceedings of the AAAI Conference on Artificial Intelligence |
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Publisher | AAAI Press |
Number | 1 |
Volume | 33 |
ISSN (Print) | 2159-5399 |
ISSN (Electronic) | 2374-3468 |
Conference
Conference | AAAI Conference on Artificial Intelligence 2019 |
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Abbreviated title | AAAI 2019 |
Country/Territory | United States of America |
City | Honolulu |
Period | 27/01/19 → 1/02/19 |
Internet address |