A New Knowledge-Transmission Based Horizontal Collaborative Fuzzy Clustering Algorithm for Unequal-Length Time Series

Abstract

This paper focuses on the clustering of unequal-length time series which appear frequently in reality. How to deal with the unequal lengths is the key step in the clustering process. In this paper, we will change the given unequal-length clustering problem into several equal-length clustering sub-problems by dividing the unequal-length time series into equal-length time series. For each sub-problem, we can use the standard fuzzy c-means algorithm to get the clustering result which is represented by a partition matrix and a set of cluster centers. In order to obtain the final clustering result of the original clustering problem, we will use the horizontal collaborative fuzzy clustering algorithm to fuse the clustering results of these sub-problems. In the process of collaboration, the collaborative knowledge is transmitted by partition matrixes whose sizes should be the same. But in the scenario here, the obtained partition matrixes most often have different sizes, thus we cannot directly use the horizontal collaborative fuzzy clustering algorithm. Taking into account the collaborative mechanism of the horizontal collaborative fuzzy clustering algorithm, this paper here presents a novel method for extending the partition matrixes to have same sizes. This method can make the partition knowledge be effectively transmitted and thus assume the good final clustering results. Experiments showed the effectiveness of the proposed method.