Two Different Methods for Initialization the I-k-Means Clustering of Time Series Data

I-k-Means is a popular clustering algorithm for time series data transformed by a multiresolution dimensionality reduction method. In this paper, we compare two different methods for initialization the I-k-means clustering algorithm. The first method uses kd tree and the second applies cluster-feature tree (CF-tree) to determine initial centers. In both approaches of clustering, we employ a new method for time series dimensionality reduction, MP_C, which can be easily made a multi-resolution feature extraction technique. Our experiments show that both initialization methods yield almost the same clustering quality, however the running time of I-k-Means initialized by using CF tree is a bit higher than that of the I-k-means initialized by using kd-tree. Both of the clustering approaches perform better than classical k-Means and I-k-Means in terms of clustering quality and running time.