Nowadays, much research leverages the clustering to mine commercial patterns from data in enterprise systems. However, previous methods cannot fully consider local structures and global topology of data, which may cause the degradation of clustering performance. To address the challenges, a deep multimodal adversarial cycle-consistent network (DMACCN) is proposed to mine intrinsic patterns of data, which can capture the local structures from instance reconstructions and the global topology from adversarial games. Specifically, DMACCN is designed as an adversarial encoding-decoding architecture composed of the modality specific-encoder, the modality-common fusion network, the cycle-consistent modality-specific generator, and the modality-fusion discriminator, which can fully fuse complementary information of data. Then, an adversarial cycle-consistent loss is devised to guide the clustering pattern mining from complementary information of data, which can align semantics between modalities and capture clustering structures of instances. The two components collaborate in a seamless manner to capture accurate commercial patterns. Finally, extensive experimental results on four datasets show DMACCN greatly outperforms the comparison methods.

中文翻译：

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智能企业系统的深度多模态对抗周期一致网络


如今，许多研究利用集群从企业系统中的数据中挖掘商业模式。然而，以前的方法不能充分考虑数据的局部结构和全局拓扑，这可能会导致聚类性能下降。为了应对这些挑战，提出了一种深度多模态对抗循环一致网络（DMACCN）来挖掘数据的内在模式，它可以从实例重建中捕获局部结构，从对抗游戏中捕获全局拓扑。具体来说，DMACCN 被设计为一种对抗性编码解码架构，由模态特定编码器、模态通用融合网络、循环一致模态特定生成器和模态融合鉴别器组成，可以充分融合模态特定编码器的互补信息。数据。然后，设计了一种对抗性循环一致损失来指导从数据的互补信息中进行聚类模式挖掘，这可以对齐模态之间的语义并捕获实例的聚类结构。这两个组件以无缝方式协作，以捕获准确的商业模式。最后，四个数据集上的广泛实验结果表明 DMACCN 大大优于比较方法。