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Mining Complex Concurrency Relationship Patterns for Dynamic Customer/Asset Interaction Modeling through Novel Industrial Behavior Networks

Funding: 2012: $140,000
2013: $120,000
2014: $120,000

Project Member(s): Zhang, C., Cao, L., Chen, L.

Funding or Partner Organisation: Coateshire (Coates Hire Pty Ltd)
Australian Research Council (ARC Linkage Projects)

Start year: 2012

Summary: Understanding customer behaviors and correlations between customers and assets are key factors for success of Business Intelligence (BI) models. With the advancement of communication, network technologies, and the global markets, major businesses are facing the challenges of understanding complex and evolutionary patterns of their customers. This project aims to transform existing tabular-based data-mining BI models to content and behavior interaction based models. As a result, the new BI models can not only consider rich content and interaction information between customers and assets, but also factor in issues, such as season, policy, and events, for supporting faster, more accurate and efficient business decision-making.

Publications:

Han, B, Tsang, IW & Chen, L 2016, 'On the Convergence of A Family of Robust Losses for Stochastic Gradient Descent', Machine Learning and Knowledge Discovery in Databases - LNCS, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD), Springer, Riva del Garda, Italy.
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Wang, H, Zhang, P, Tsang, I, Chen, L & Zhang, C 2015, 'Defragging Subgraph Features for Graph Classification', Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, ACM International Conference on Information and Knowledge Management, ACM, Melbourne, VIC, Australia, pp. 1687-1690.
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Yang, W, Gao, Y & Cao, L 2013, 'TRASMIL: A local anomaly detection framework based on trajectory segmentation and multi-instance learning', Computer Vision And Image Understanding, vol. 117, no. 10, pp. 1273-1286.
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Liu, B, Xiao, Y, Yu, P, Cao, L & Hao, Z 2013, 'Robust Textual Data Streams Mining Based on Continuous Transfer Learning', Proceedings of the 13th SIAM International Conference on Data Mining, SIAM International Conference on Data Mining, SIAM, Austin, Texas, USA, pp. 731-739.
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Liu, C, Chen, L & Zhang, C 2013, 'Mining Probabilistic Representative Frequent Patterns From Uncertain Data', The 13th SIAM International Conference on Data Mining (SDM 2013), SIAM International Conference on Data Mining, SIAM / Omnipress, Austin, Texas, USA, pp. 1-9.
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Liu, C, Chen, L & Zhang, C 2013, 'Summarizing Probabilistic Frequent Patterns: A Fast Approach', Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD13), ACM International Conference on Knowledge Discovery and Data Mining, ACM, Chicago, Illinois USA, pp. 527-535.
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Wan, L, Chen, L & Zhang, C 2013, 'Mining Dependent Frequent Serial Episodes from Uncertain Sequence Data', Proceedings of the13th IEEE International Conference on Data Mining, IEEE International Conference on Data Mining, IEEE Computer Society Press, Dallas, TX, USA, pp. 1211-1216.
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Long, G, Chen, L, Zhu, X & Zhang, C 2012, 'TCSST: transfer classification of short & sparse text using external data', Proc. Of The 21st ACM Conference on Information and Knowledge Management (CIKM-12), ACM International Conference on Information and Knowledge Management, ACM, Maui, Hawaii, USA, pp. 764-772.
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Keywords: data mining and machine learning;business intelligence;behavior modeling and interaction analysis

FOR Codes: Pattern Recognition and Data Mining, Application Software Packages (excl. Computer Games), Business Information Management (incl. Records, Knowledge and Information Management, and Intelligence), Property, Business Support Services and Trade not elsewhere classified, Information Processing Services (incl. Data Entry and Capture)