1 research outputs found
Clustering Time-Series by a Novel Slope-Based Similarity Measure Considering Particle Swarm Optimization
Recently there has been an increase in the studies on time-series data mining
specifically time-series clustering due to the vast existence of time-series in
various domains. The large volume of data in the form of time-series makes it
necessary to employ various techniques such as clustering to understand the
data and to extract information and hidden patterns. In the field of clustering
specifically, time-series clustering, the most important aspects are the
similarity measure used and the algorithm employed to conduct the clustering.
In this paper, a new similarity measure for time-series clustering is developed
based on a combination of a simple representation of time-series, slope of each
segment of time-series, Euclidean distance and the so-called dynamic time
warping. It is proved in this paper that the proposed distance measure is
metric and thus indexing can be applied. For the task of clustering, the
Particle Swarm Optimization algorithm is employed. The proposed similarity
measure is compared to three existing measures in terms of various criteria
used for the evaluation of clustering algorithms. The results indicate that the
proposed similarity measure outperforms the rest in almost every dataset used
in this paper.Comment: 27 pages, 8 figures, 12 table