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37 results on '"Luo, Chao"'

34. Outlier Mining on Multiple Time Series Data in Stock Market.

Author

Luo, Chao, Zhao, Yanchang, Cao, Longbing, Ou, Yuming, and Liu, Li

Abstract

With the dramatic increase of stock market data, traditional outlier mining technologies have shown their limitations in efficiency and precision. In this paper, an outlier mining model on stock market data is proposed, which aims to detect the anomalies from multiple complex stock market data. This model is able to improve the precision of outlier mining on individual time series. The experiments on real-world stock market data show that the proposed outlier mining model is effective and outperforms traditional technologies. [ABSTRACT FROM AUTHOR]

Published
2008
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35. Domain-Driven Local Exceptional Pattern Mining for Detecting Stock Price Manipulation.

Author

Ou, Yuming, Cao, Longbing, Luo, Chao, and Zhang, Chengqi

Abstract

Recently, a new data mining methodology, Domain Driven Data Mining (D3M), has been developed. On top of data-centered pattern mining, D3M generally targets the actionable knowledge discovery under domain-specific circumstances. It strongly appreciates the involvement of domain intelligence in the whole process of data mining, and consequently leads to the deliverables that can satisfy business user needs and decision-making. Following the methodology of D3M, this paper investigates local exceptional patterns in real-life microstructure stock data for detecting stock price manipulations. Different from existing pattern analysis mainly on interday data, we deal with tick-by-tick data. Our approach proposes new mechanisms for constructing microstructure order sequences by involving domain factors and business logics, and for measuring the interestingness of patterns from business concern perspective. Real-life data experiments on an exchange data demonstrate that the outcomes generated by following D3M can satisfy business expectations and support business users to take actions for market surveillance. [ABSTRACT FROM AUTHOR]

Published
2008
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36. Agent-Mining Interaction: An Emerging Area.

Author

Carbonell, Jaime G., Siekmann, Jörg, Gorodetsky, Vladimir, Skormin, Victor A., Cao, Longbing, Luo, Chao, and Zhang, Chengqi

Abstract

In the past twenty years, agents (we mean autonomous agent and multi-agent systems) and data mining (also knowledge discovery) have emerged separately as two of most prominent, dynamic and exciting research areas. In recent years, an increasingly remarkable trend in both areas is the agent-mining interaction and integration. This is driven by not only researcher's interests, but intrinsic challenges and requirements from both sides, as well as benefits and complementarity to both communities through agent-mining interaction. In this paper, we draw a high-level overview of the agent-mining interaction from the perspective of an emerging area in the scientific family. To promote it as a newly emergent scientific field, we summarize key driving forces, originality, major research directions and respective topics, and the progression of research groups, publications and activities of agent-mining interaction. Both theoretical and application-oriented aspects are addressed. The above investigation shows that the agent-mining interaction is attracting everincreasing attention from both agent and data mining communities. Some complicated challenges in either community may be effectively and efficiently tackled through agent-mining interaction. However, as a new open area, there are many issues waiting for research and development from theoretical, technological and practical perspectives. [ABSTRACT FROM AUTHOR]

Published
2007
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37. A Framework for Relational Link Discovery.

Author

Zhang, Shichao, Jarvis, Ray, Luo, Dan, Luo, Chao, and Zhang, Chunzhi

Abstract

Link discovery is an emerging research direction for extracting evidences and links from multiple data sources. This paper proposes a self-organizing framework for discovering links from multi-relational databases. It includes main functional modules for developing adaptive data transformers and representation specification, multi-relational feature construction, and self-organizing multi-relational correlation and link discovery algorithms. [ABSTRACT FROM AUTHOR]

Published
2005
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