Techniques for Measuring Business Process Based on Business Values

Lee, Ji‐Hyun; Kang, Sungwon

doi:10.1587/transinf.2014edp7330

IEICE Trans. Inf. & Syst.

2015

DOI: 10.1587/transinf.2014edp7330

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Techniques for Measuring Business Process Based on Business Values

Ji‐Hyun Lee

Sungwon Kang

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“…Non-negative matrix factorization (NMF, [3][4][5][6]). NMF can be expressed as follows: given a non-negative objective matrix X of size n × m, columns of which are samples and rows are features, we try to find two non-negative matrices F of size n × k and G of size m × k such that X ≈ F G T .…”

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confidence: 99%

Community structure detection in complex networks with partial background information

Zhang

Sun

Wang

2013

EPL

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Constrained clustering has been well-studied in the unsupervised learning society. However, how to encode constraints into community structure detection, within complex networks, remains a challenging problem. In this paper, we propose a semi-supervised learning framework for community structure detection. This framework implicitly encodes the must-link and cannot-link constraints by modifying the adjacency matrix of network, which can also be regarded as de-noising the consensus matrix of community structures. Our proposed method gives consideration to both the topology and the functions (background information) of complex network, which enhances the interpretability of the results. The comparisons performed on both the synthetic benchmarks and the real-world networks show that the proposed framework can significantly improve the community detection performance with few constraints, which makes it an attractive methodology in the analysis of complex networks.

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confidence: 99%

Community structure detection in complex networks with partial background information

Zhang

Sun

Wang

2013

EPL

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Techniques for Measuring Business Process Based on Business Values

Cited by 1 publication

References 14 publications

Community structure detection in complex networks with partial background information

Community structure detection in complex networks with partial background information

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