Nearest-Neighbour-Induced Isolation Similarity and its Impact on Density-Based Clustering
This work addresses clustering accuracy for data analysis, but it is incremental as it modifies an existing similarity measure and applies it to a classic algorithm.
The authors tackled the problem of improving density-based clustering by proposing a nearest-neighbor method to implement Isolation Similarity, which significantly uplifts DBSCAN's performance to surpass that of the recent DP algorithm.
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.