In most law enforcement cases, judgment debtors have behaviors of evading execution in China, which seriously affects the authority of legal judgments and the judiciary's credibility. Characteristics analysis of judgment debtors plays a vital role in finding out the concealed property and improving efficiency in handling law enforcement cases. Considering the advantages of hesitant fuzzy linguistic term sets (HFLTSs) representing the judgment debtors' attributes and keeping all the original evaluation information on judgment debtors, we develop a hesitant fuzzy linguistic agglomerative hierarchical clustering (HFL-AHC) method to cluster judgment debtors and analyze the main characteristic of judgment debtors with concealing property. In some situations, the existing HFLTS distance cannot divide the judgment debtors. Therefore, we propose some new distance measures to classify the judgment debtors. The clustering results show that the judgment debtors who hide property have a poor evaluation of trading behavior, work, credibility, and consumption behavior.INDEX TERMS hesitant fuzzy linguistic term sets (HFLTSs); agglomerative hierarchical clustering (AHC) method; distance measure; judgment debtors; law enforcement H. Zhang et al: Characteristic analysis of judgment debtors H. Zhang et al: Characteristic analysis of judgment debtors 2 VOLUME XX, 2017 Expert judges Evaluation information of judgment debtors attritubes Construct the distance matrix between each two judgment debtors D=(dij)m*m Merge two clusters with the minimum distance and update the centers of all the clustersUntil all the judgment debtors aggregate into one cluster
Input informationClustering analysis of judgment debtors by HFL-AHC method
Judgment debtors attributes
HFLTSsConstruct the HFLTS matrix of judgment debtors attributes
Output informationClustering result and judgment debtors characteristics analysis H. Zhang et al: Characteristic analysis of judgment debtors 2 VOLUME XX, 2017 H. Zhang et al: Characteristic analysis of judgment debtors 2 VOLUME XX, 2017