The effectiveness and efficiency of the existing cluster analysis methods are limited, especially when the referred data has high dimensions or when the clusters within the data are not well-separated and having different densities, sizes and shapes. Density-based clustering algorithms have been proven able to discovered clusters with those characteristics. Previous researchers which explored d...

CFSFDP (clustering by fast search and find of density peaks) is recently developed density-based clustering algorithm. Compared to DBSCAN, it needs less parameters and is computationally cheap for its noniteration. Alex. at al have demonstrated its power by many applications. However, CFSFDP performs not well when there are more than one density peak for one cluster, what we name as "no density...

This paper presents a comparative study of three Density based Clustering Algorithms that are DENCLUE, DBCLASD and DBSCAN. Six parameters are considered for their comparison. Result is supported by firm experimental evaluation. This analysis helps in finding the appropriate density based clustering algorithm in variant situations. General Terms Algorithms .

This paper intend to present an approach to analyse the change of word meaning based on word embedding, which is a more general method to quantize words than before. Through analysing the similar words and clustering in different period, semantic change could be detected. We analysed the trend of semantic change through density clustering method called DBSCAN. Statics and data visualization is ...

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DBSCAN is widely used in various fields, but it requires computational costs similar to those of re-clustering from scratch update clusters when new data inserted. To solve this, we propose an incremental density-based clustering method that rapidly updates by identifying advance regions where cluster will occur. Also, through extensive experiments, show our provides results DBSCAN.

This paper presents a new PSO-based optimization DBSCAN space clustering algorithm with obstacle constraints. The algorithm introduces obstacle model and simplifies two-dimensional coordinates of the cluster object coding to one-dimensional, then uses the PSO algorithm to obtain the shortest path and minimum obstacle distance. At the last stage, this paper fulfills spatial clustering based on o...