基于斜率相似性测度的时间序列聚类粒子群算法
Clustering Time-Series by a Novel Slope-Based Similarity Measure Considering Particle Swarm Optimization
摘要：
由于时间序列在各个领域的广泛存在，近年来对时间序列数据采掘的研究越来越多，特别是时间序列聚类。时间序列形式的大量数据使得有必要采用诸如聚类的各种技术来理解数据并提取信息和隐藏模式。在聚类领域，特别是时间序列聚类中，最重要的方面是所使用的相似性度量和用于进行聚类的算法。在本文中，一个新的相似性度量的时间序列聚类的基础上开发的一个简单的时间序列表示，斜率的时间序列的每一段，欧氏距离和所谓的动态时间弯曲的组合。本文证明了所提出的距离度量是度量的，从而可以应用索引。对于聚类任务，采用粒子群优化算法。建议的相似性度量进行比较，现有的三种措施，用于评估聚类算法的各种标准。结果表明，该方法在几乎所有的数据集上都优于其他方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
