Clustering Time-Series by a Novel Slope-Based Similarity Measure Considering Particle Swarm Optimization

TL;DR

This paper introduces a new slope-based similarity measure for time-series clustering that is metric and combined with Particle Swarm Optimization, outperforming existing measures across multiple datasets.

Contribution

A novel metric similarity measure based on slope, Euclidean distance, and dynamic time warping for improved time-series clustering, integrated with Particle Swarm Optimization.

Findings

Proposed measure outperforms existing similarity measures in most datasets.

The similarity measure is proven to be metric, enabling indexing.

Particle Swarm Optimization effectively clusters time-series using the new measure.

Abstract

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.