Herd Clustering: A synergistic data clustering approach using collective intelligence

• New view angle: we can make data easy to be interpreted by ‘vitalizing’ data.
• To demonstrate the concept, Herd Clustering (HC) is proposed and described.
• Comprehensive analysis is conducted to observe the behavior of HC.
• Comparisons are conducted to demonstrate the uniqueness of HC.
• A real-world application is conducted to demonstrate the applicability of HC.

Traditional data mining methods emphasize on analytical abilities to decipher data, assuming that data are static during a mining process. We challenge this assumption, arguing that we can improve the analysis by vitalizing data. In this paper, this principle is used to develop a new clustering algorithm.Inspired by herd behavior, the clustering method is a synergistic approach using collective intelligence called Herd Clustering (HC). The novel part is laid in its first stage where data instances are represented by moving particles. Particles attract each other locally and form clusters by themselves as shown in the case studies reported. To demonstrate its effectiveness, the performance of HC is compared to other state-of-the art clustering methods on more than thirty datasets using four performance metrics. An application for DNA motif discovery is also conducted. The results support the effectiveness of HC and thus the underlying philosophy.

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