Clustering is used to gain an intuition of the structures in the data.Most of the current clustering algorithms produce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterabllity measure is proposed in this paper.It measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningful insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evaluated against several synthetic data sets and time-series data sets,which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.