ITM Web Conf.
Volume 12, 2017The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|Number of page(s)||3|
|Section||Session 5: Information Processing Methods and Techniques|
|Published online||05 September 2017|
A Weight-Based Clustering Method
Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
This paper proposes a weight-based self-constructing clustering method for time series data. Self-constructing clustering processes all the data points incrementally. If a data point is not similar enough to an existing cluster, then (1) if the point currently does not belong to any cluster, it forms a new cluster of its own; (2) otherwise, the point is removed from the cluster it currently belongs to before a new cluster is formed. However, if a data point is similar enough to an existing cluster, then (1) if the point currently does not belong to any cluster, it is added to the most similar cluster; (2) otherwise, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned and considered in the calculations of similarity between data points and clusters. Experimental results show that our proposed approach performs more effectively than other methods for real world time series datasets.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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