A Systemic Review on RFID Clustering And Similarity Measures : Issues and Challenges

recent advances in technologies such as radio-frequency identification (RFID) have made automatic tracking and tracing possible in a broad range of applications. Due to the significance of automatic tracking by RFID, trajectory clustering is an important topic in RFID data management and mining, which has a broad range of applications in various areas, such as traffic monitoring, video surveillance, cattle tracking and supply chain management. Trajectory clustering is the process of grouping similar trajectories according to a similarity distance. Depending on the task, given a set of trajectories, one may want to find clusters of objects that followed the same path or detect groups that moved together for given period. For trajectory tracking, literature presents different algorithms like Time-Focused Clustering (TFC) algorithm and Fuzzy C-Means algorithm. One of the recent method presented in the literature is Hierarchical RFID Trajectory Clustering [9] which considered a similarity measure called, Time-parameterized Edit Distance (TED). This type was used to find the similarity between to trajectory path by taking into consideration time dimension values in the calculation. Also, this model can deal with variants in both time and space dimensions, and the clustering algorithm is much less sensitive to noise and outliers than existing methods. But, similarity measure considered for clustering does not deem the weightage for the different parameters. TED grants the same weightage for all the parameters.