An Effective Algorithm for Clustering Time-Series Data by Fuzzy Logic
Techniques for evaluating the similarity between time series data-sets have long been of interest to the database community. New location-based applications that generate time series location trails (called trajectories) have also fueled interest in this topic since time series similarity methods can be used for computing trajectory similarity. One of the critical research issues with time series analysis is the choice of distance function to capture the notion of similarity between two sequences. The authors will also present the benefits of the proposed system by implementing a real application: using dataset that contains two attributes temperature and humidity.