A novel multi-label clustering method based on distance measure

作者：Sun Tie Li; Cheng Si Ying; Wu Di; Sun Xiao Xin; Yang Feng Qin^*

来源：ICIC Express Letters, 2012, 6(5): 121-126.

• Benchmark datasets
• Clustering methods
• Data points
• Data sets
• Distance measure
• F-measure
• Key techniques
• Multi-label
• Multiple clusterings
• Traditional clustering

Clustering is one of the key techniques in data mining. Almost, traditional clustering methods just focus on single-label datasets but do not concern the multi-label problem, where a data point can belong to more than one cluster. In this paper, we propose a new multi-label clustering method based on distance measure (MCDM). F-measure is a widely used clustering validation measure, but its value may be out of the range [0, 1] in multi-label clustering. We revise F-measure in order to guarantee the value is in the range [0, 1]. Experiments on benchmark datasets demonstrate that the multiple clustering we proposed is effective and the revision of F-measure is reasonable.

• 单位
  东北师范大学