Performing analytic of household load curves (LCs) has significant value in predicting individual electricity consumption patterns, and hence facilitate developing demand-response strategy, and finally achieve energy efficiency improvement and emission reduction. LC clustering is a widely used analytic technology, which discovers electricity consumption patterns by grouping similar LCs into same sub-groups. However, previous clustering methods treat each LC in the data set as an individual time series, ignoring the inherent relationship among different LCs, restraining the performance of the clustering methods. What's more, due to the significant volatility and uncertainty of LCs, the previous LC clustering approaches inevitably result in either lager number of clusters or huge variances within a cluster, which is unacceptable for actual application needs. In this paper, we proposed an integrated approach to address this issue. First, we converted the LC clustering task into the community detection task in network. Second, we proposed a clustering approach incorporated with community detection to improve the performance of LC clustering, which includes network construction, community detection and typical load profile extraction. The method convert the data set into a network in which the inherent relationship among LCs is represented by the edges of the network. Third, we construct a multi-layer typical load profile directory to make the trade-off between variances within a cluster and the number of the clusters, enabling the researchers to assess the LCs and the customers in different layers according to practical application requirements. The experiments demonstrate that our integrated approach outperform the state-of-the-art methods.