A Novel Streaming Data Clustering Algorithm Based on Fitness Proportionate Sharing

Abstract

As an unsupervised learning technique, clustering can effectively capture the patterns in a data stream based on similarities among the data. Traditional data stream clustering algorithms either heavily depend on some prior knowledge or predefined parameters while the characteristics of real-time data are considered unknown. Besides, the user-specified threshold is used to overcome the effect of outliers and noises, which significantly affects the clustering performance. The overlap among clusters is another major challenge for the existing stream clustering methods. These constraints strongly limit their real-time applications. In this paper, a two-phase stream clustering algorithm based on fitness proportionate sharing is proposed. It handles streaming data when prior knowledge is not available and maps the clustering problem into a multimodal optimization problem. It introduces a density-based objective function and adopts the fitness proportionate sharing strategy to perform a more effective search for the cluster centers. To capture the dynamic characteristics of streaming data, a recursive formula for the lower bound of the density function is derived, and a summary of historical data is established for the proposed algorithm. The proposed algorithm is applied to different data sets, and a comprehensive comparison between the proposed algorithm and five well-known data stream clustering algorithms in the literature is provided. Results show comparable or better performance relative to five popular data stream clustering algorithms. A scalability analysis of the proposed streaming clustering method is presented in this paper as well.INDEX TERMS Data streams, clustering, unsupervised learning, data mining. I. INTRODUCTION

Notice of Retraction: An Improved High-Density Sub Trajectory Clustering Algorithm

“…Compared to CFSFDP, the proposed algorithm can obtain d c automatically and use sparse learning to determine the neighbors of each data point, removing irrelevant data points at the same time. Yan et al [35] proposed a new clustering algorithm based on fitness proportionate sharing to map the problem into a multimodal optimization problem. The individuals with the highest density values were the cluster centers, the fitness proportionate sharing strategy was implemented in the identification results to overcome the sensitivity of uneven density values of cluster centers.…”

“…The sub-trajectory segments are clustered based on different cutoff distances. The value range of d c in the city 1 trajectory dataset is [20], [30], and the value range of d c in the city 2 trajectory dataset is [25], [35]. Comparing the trend of the number of clusters corresponding to different cutoff distance values, the results are shown in Figure 12.…”

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