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Abstract:
We propose an interest support based subspace clustering approach to improving the performance of predicting interesting behaviors. The approach uses particular heuristics to discover interesting subspace clusters more efficiently and selects a proper set of subspace clusters to build a cluster based classification model. We evaluate our approach using synthetic data and a real world application of predicting customer repurchase behavior. The experimental results show our approach is effective in discovering interesting and interpretable clusters that provide better prediction on customer repurchase behavior.