Turning Clusters into Patterns: Rectangle-Based Discriminative Data Description
作者： Byron Gao , Martin Ester
关键词: Discriminative model 、 Cluster analysis 、 Data description 、 Cluster (physics) 、 Knowledge extraction 、 Machine learning 、 Rectangle 、 Artificial intelligence 、 Data mining 、 Computer science 、 Interpretability 、 Heuristic
摘要: The ultimate goal of data mining is to extract knowledge from massive data. Knowledge ideally represented as human-comprehensible patterns which end-users can gain intuitions and insights. Yet not all methods produce such readily understandable knowledge, e.g., most clustering algorithms output sets points clusters. In this paper, we perform a systematic study cluster description that generates interpretable We introduce analyze novel formats leading more expressive power, motivate define problems specifying different trade-offs between interpretability accuracy. also present effective heuristic together with their empirical evaluations.
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