Incremental Clustering for Time Series Data based on an Improved Leader Algorithm

Incremental clustering is one of the most important tasks in streaming data mining. As for streaming time series, incremental clustering can be used in some advanced time series data mining tasks such as anomaly detection and motif discovery. In this paper, we propose a novel incremental clustering method for time series. The proposed method is an improved variant of the Leader algorithm, called I-Leader. Leader is a well-known incremental clustering method which is a single-pass distance-based partitional clustering method. However, Leader suffers from some weaknesses such as producing not good clustering result which consists of a large number of clusters and therefore requiring a lot of time for clustering. To overcome these weaknesses of Leader, in I-Leader we employ a data summarization technique and use this data summarization to maintain the high quality for the clusters. We have conducted some experiments on several datasets to compare the performance of the I-Leader algorithm with that of the original Leader in time series clustering. Besides, we compare I-Leader with k-Means. The experimental results show that our I-Leader algorithm runs faster than both Leader and k-Means. I-Leader is also more effective than both of these algorithms with better clustering results.