关键词: 聚类, 时间序列, k-means算法

Abstract: Time series clustering is an important algorithm widely used by many applications, such as the analysis and forecast of topics on social media and search words on search engine. However, existing time series clustering algorithms suffer from two shortcomings. Firstly, time series clustering algorithms mostly work only for isometric time series with equal length, leading to the loss of many important features and negative impact of clustering results. Secondly, time series similarity metrics are not able to compare the shape similarity of time series. To address the problems, this paper proposes a novel computation framework to cluster time series data with non-equal length. At first, this paper uses z_score standardization to normalize the observed values of time series data. Next, based on sliding window, this paper extends STS (short time series) distance and designs a new distance measure for time series with non-equal time length. After that, this paper adapts the classic k-means algorithm to develop a new clustering algorithm. The extensive experimental results, by two real datasets that are collected from search engines and public data, successfully verify that the proposed time series clustering algorithm can handle non-equal time series data and outperform the state of arts in terms of clustering accuracy and quality.

Key words: clustering, time series, k-means algorithm