A time series is a sequence of real data, representing the measurements of a real variable at time intervals. Time series analysis is a sufficiently well-known task; however, in recent years research has been carried out with the purpose to try to use clustering for the intentions of time series analysis. The main motivation for representing a time series in the form of clusters is to better represent the main characteristics of the data. The central goal of the present research paper was to investigate clustering methodology for time series data mining, to explore the facilities of time series similarity measures and to use them in the analysis of time series clustering results. More complicated similarity measures include Longest Common Subsequence method (LCSS). In this paper, two tasks have been completed. The first task was to define time series similarity measures. It has been established that LCSS method gives better results in the detection of time series similarity than the Euclidean distance. The second task was to explore the facilities of the classical k-means clustering algorithm in time series clustering. As a result of the experiment a conclusion has been drawn that the results of time series clustering with the help of k-means algorithm correspond to the results obtained with LCSS method, thus the clustering results of the specific time series are adequate.
Kirchgassner G., Wolter J. Introduction to modern time series analysis. - Berlin:Springer, 2007, 274 p.
Lutkepohl H. New introduction to multiple time series analysis. - Berlin:Springer, 2005, 764 p.
Tsay R.S. Analysis of financial time series. - John Wiley & Sons, 2002, 448 p.
Vlachos M., Gunopulos D. Indexing time series under condition of noise. Data mining in time series database: Series in machine perception and artificial intelligence. - World Scientific Publishing, 2004. Vol.57, pp. 67-100.
Agrawal R., Faloutsos C., Swami A. Efficient similarity search in sequence databases. Proc. 4th Int. Conf. On Foundations of Data Organizations and Algorithms, 1993. - Chicago. pp. 69-84.
Faloutsos C., Ranganathan M., Manolopoulos Y. Fast subsequence matching in time-series databases. Proc. ACM SIGMOID Int. Conf. on Management of Data, 1994. - Minneapolis. pp. 419 - 429.
Keogh E., Lin J., Truppel W. Clustering of time series subsequences in meaningless implications for previous and future research. Proc. of the 3rd IEEE International Conference on Data Mining, 2003. - pp. 115 - 122.
Xu R., Wunch D.C. Clustering. - John Wiley & Sons, 2009, 358 p.
Fujimaki R., Hirose S., Nakata T. Theoretical analysis of subsequence time-series clustering from a frequency-analysis viewpoint. SIAM International Conference on Data Mining, 2008. - Atlanta. pp. 506 - 517.