An Effective Algorithm for Clustering Time-Series Data by Fuzzy Logic
Sadiq Hussain · 2011
A variety of techniques currently exist for measuring the similarity between time series datasets. Of these techniques, the methods whose matching criteria are bounded by a specified threshold value, such as the LCSS technique, have been shown to be robust in the presence of noise, time shifts, and data scaling. Afterwards, by utilizing an efficient method, clusters are updated incrementally and periodically through a set of fuzzy approaches. So, in this paper, we utilize the clustering of time series data using LCSS and Fuzzy Logic. In addition, we will present the benefits of the proposed system by implementing a real application: using dataset that contains two attributes temperature and humidity.