A new local density and relative distance based spectrum clustering

Abstract

A novel local density and relative distance-based spectrum clustering (LDRDSC) algorithm is proposed for multidimensional data clustering. The density spectra consider both redefined local densities and relative distances. The spectral peaks are defined as cluster centers since these peaks correspond to the local density maximums. Different clusters correspond to different spectra. The clustering by fast search and find of density peaks (CFSFDP) algorithm and several benchmark data sets are employed to validate our proposed LDRDSC algorithm. Once the density spectrum is generated, the rest points can be automatically clustered by our LDRDSC algorithm, which is different from CFSFDP. CFSFDP needs to categorize data points according to the cluster centers. Furthermore, our LDRDSC algorithm is compared with other five typical clustering algorithms (DBSCAN, FCM, AP, Mean Shift and k-means) in order to validate the effectiveness of the proposed algorithm. Computational results demonstrate that our algorithm can obtain a better clustering result than the above mentioned algorithms, especially in identifying noises or isolates.

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