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 ...

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a r t i c l e i n f o a b s t r a c t The success of the Case Based Reasoning system depends on the quality of the case data...

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...

DBSCAN is arguably the most popular density-based clustering algorithm, and it capable of recovering non-spherical clusters. One its main weaknesses that treats all features equally. In this paper, we propose a algorithm calculating feature weights representing degree relevance each feature, which takes density structure data into account. First, improve introduce new called DBSCANR. DBSCANR re...