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Displaying 1-45 out of 45 total
Behavior Informatics: A New Perspective
Found in: IEEE Intelligent Systems
By Longbing Cao,Thorsten Joachims,Can Wang,Eric Gaussier,Jinjiu Li,Yuming Ou,Dan Luo,Reza Zafarani,Huan Liu,Guandong Xu,Zhiang Wu,Gabriella Pasi,Ya Zhang,Xiaokang Yang,Hongyuan Zha,Edoardo Serra,V.S. Subrahmanian
Issue Date:July 2014
pp. 62-80
This installment of Trends & Controversies provides an array of perspectives on the latest research in behavior informatics. Longbing Cao introduces the work in "Behavior Informatics: A New Perspective." Then, in "Behavior Computing,...
 
Multi-strategy Integration for Actionable Trading Agents
Found in: Web Intelligence and Intelligent Agent Technology, International Conference on
By Longbing Cao
Issue Date:November 2007
pp. 487-490
Trading agents are very useful for developing and back-testing quality trading strategies to support smart trading actions in the market. However, the existing trading agent research mainly focuses on simple and simulated strategies. As a result, there exi...
 
Guest Editors' Introduction: Agents and Data Mining
Found in: IEEE Intelligent Systems
By Longbing Cao, Vladimir Gorodetsky, Pericles A. Mitkas
Issue Date:May 2009
pp. 14-15
On top of two active research streams, agents and data mining, a most recent and exciting trend is their interaction and integration. Agent mining has emerged as a very promising field due to its unique contributions to complementary and innovative methodo...
 
Domain-Driven, Actionable Knowledge Discovery
Found in: IEEE Intelligent Systems
By Longbing Cao, Chengqi Zhang, Qiang Yang, David Bell, Michail Vlachos, Bahar Taneri, Eamonn Keogh, Philip S. Yu, Ning Zhong, Mafruz Zaman Ashrafi, David Taniar, Eugene Dubossarsky, Warwick Graco
Issue Date:July 2007
pp. 78-88, c3
Existing knowledge discovery and data mining (KDD) field seldom deliver results that businesses can act on directly. This issue, Trends & Controversies presents seven short articles reporting on different aspects of domain-driven KDD, an R&...
 
An Efficient Approach for Outlier Detection with Imperfect Data Labels
Found in: IEEE Transactions on Knowledge and Data Engineering
By Bo Liu, Yanshan Xiao,Philip S. Yu, Zhifeng Hao, Longbing Cao
Issue Date:July 2014
pp. 1-1
The task of outlier detection is to identify data objects that are markedly different from or inconsistent with the normal set of data. Most existing solutions typically build a model using the normal data and identify outliers that do not fit the represen...
 
Uncertain One-Class Learning and Concept Summarization Learning on Uncertain Data Streams
Found in: IEEE Transactions on Knowledge and Data Engineering
By Bo Liu,Yanshan Xiao,Philip S. Yu,Longbing Cao,Yun Zhang,Zhifeng Hao
Issue Date:February 2014
pp. 468-484
This paper presents a novel framework to uncertain one-class learning and concept summarization learning on uncertain data streams. Our proposed framework consists of two parts. First, we put forward uncertain one-class learning to cope with data of uncert...
 
Leveraging Supervised Label Dependency Propagation for Multi-label Learning
Found in: 2013 IEEE International Conference on Data Mining (ICDM)
By Bin Fu,Guandong Xu,Zhihai Wang,Longbing Cao
Issue Date:December 2013
pp. 1061-1066
Exploiting label dependency is a key challenge in multi-label learning, and current methods solve this problem mainly by training models on the combination of related labels and original features. However, label dependency cannot be exploited dynamically a...
 
Efficiently Mining Top-K High Utility Sequential Patterns
Found in: 2013 IEEE International Conference on Data Mining (ICDM)
By Junfu Yin,Zhigang Zheng,Longbing Cao,Yin Song,Wei Wei
Issue Date:December 2013
pp. 1259-1264
High utility sequential pattern mining is an emerging topic in the data mining community. Compared to the classic frequent sequence mining, the utility framework provides more informative and actionable knowledge since the utility of a sequence indicates b...
 
The Foundation of Fuzzy Rule Interchange in the Semantic Web
Found in: 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)
By Xing Wang,Ji Chen,Longbing Cao,Xiangfu Meng
Issue Date:November 2013
pp. 280-281
RIF (Rule Interchange Format) is a W3C's recommendation and an appropriate intermediary language for crisp (i.e., non fuzzy) rule interchange in the Semantic Web, but it is incapable of representing and interchanging fuzzy rules. Therefore, combining RIF a...
 
Coupled clustering ensemble: Incorporating coupling relationships both between base clusterings and objects
Found in: 2013 IEEE International Conference on Data Engineering (ICDE 2013)
By Can Wang,Zhong She,Longbing Cao
Issue Date:April 2013
pp. 374-385
Clustering ensemble is a powerful approach for improving the accuracy and stability of individual (base) clustering algorithms. Most of the existing clustering ensemble methods obtain the final solutions by assuming that base clusterings perform independen...
 
Coupled Behavior Analysis with Applications
Found in: IEEE Transactions on Knowledge and Data Engineering
By Longbing Cao,Yuming Ou,Philip S. Yu
Issue Date:August 2012
pp. 1378-1392
Coupled behaviors refer to the activities of one to many actors who are associated with each other in terms of certain relationships. With increasing network and community-based events and applications, such as group-based crime and social network interact...
 
Vote-Based LELC for Positive and Unlabeled Textual Data Streams
Found in: Data Mining Workshops, International Conference on
By Bo Liu, Yanshan Xiao, Longbing Cao, Philip S. Yu
Issue Date:December 2010
pp. 951-958
In this paper, we extend LELC (PU Learning by Extracting Likely Positive and Negative Micro-Clusters) method to cope with positive and unlabeled data streams. Our developed approach, which is called vote-based LELC, works in three steps. In the first step,...
 
SMILE: A Similarity-Based Approach for Multiple Instance Learning
Found in: Data Mining, IEEE International Conference on
By Yanshan Xiao, Bo Liu, Longbing Cao, Jie Yin, Xindong Wu
Issue Date:December 2010
pp. 589-598
Multiple instance learning (MIL) is a generalization of supervised learning which attempts to learn useful information from bags of instances. In MIL, the true labels of the instances in positive bags are not always available for training. This leads to a ...
 
Exploiting Local Data Uncertainty to Boost Global Outlier Detection
Found in: Data Mining, IEEE International Conference on
By Bo Liu, Jie Yin, Yanshan Xiao, Longbing Cao, Philip S. Yu
Issue Date:December 2010
pp. 304-313
This paper presents a novel hybrid approach to outlier detection by incorporating local data uncertainty into the construction of a global classifier. To deal with local data uncertainty, we introduce a confidence value to each data example in the training...
 
Less Effort, More Outcomes: Optimising Debt Recovery with Decision Trees
Found in: Data Mining Workshops, International Conference on
By Yanchang Zhao, Hans Bohlscheid, Shanshan Wu, Longbing Cao
Issue Date:December 2010
pp. 655-660
This paper presents a real-world application of data mining techniques to optimise debt recovery in social security. The traditional method of contacting a customer for the purpose of putting in place a debt recovery schedule has been an out-bound phone ca...
 
i-Analyst: An Agent-Based Distributed Data Mining Platform
Found in: Data Mining Workshops, International Conference on
By Chayapol Moemeng, Xinhua Zhu, Longbing Cao, Chen Jiahang
Issue Date:December 2010
pp. 1404-1406
User-friendliness and performance are important properties of data mining and analysis tools. In this demo, we introduced an agent-based distributed data mining platform that allows users to manage and share the data-mining-related resources conveniently. ...
 
Domain-Driven Data Mining: Challenges and Prospects
Found in: IEEE Transactions on Knowledge and Data Engineering
By Longbing Cao
Issue Date:June 2010
pp. 755-769
Traditional data mining research mainly focus]es on developing, demonstrating, and pushing the use of specific algorithms and models. The process of data mining stops at pattern identification. Consequently, a widely seen fact is that 1) many algorithms ha...
 
A Cost-Effective LSH Filter for Fast Pairwise Mining
Found in: Data Mining, IEEE International Conference on
By Gang Zhao, Yun Xiong, Longbing Cao, Dan Luo, Xuchun Su, Yangyong Zhu
Issue Date:December 2009
pp. 1088-1093
The pairwise mining problem is to discover pairwise objects having measures greater than the user-specified minimum threshold from a collection of objects. It is essential in a large variety of database and data-mining applications. Of late, there has been...
 
Flexible Frameworks for Actionable Knowledge Discovery
Found in: IEEE Transactions on Knowledge and Data Engineering
By Longbing Cao, Yanchang Zhao, Huaifeng Zhang, Dan Luo, Chengqi Zhang, E.K. Park
Issue Date:September 2010
pp. 1299-1312
Most data mining algorithms and tools stop at the mining and delivery of patterns satisfying expected technical interestingness. There are often many patterns mined but business people either are not interested in them or do not know what follow-up actions...
 
Agent Mining: The Synergy of Agents and Data Mining
Found in: IEEE Intelligent Systems
By Longbing Cao, Vladimir Gorodetsky, Pericles A. Mitkas
Issue Date:May 2009
pp. 64-72
Autonomous agents and multiagent systems (or agents) and data mining and knowledge discovery (or data mining) are two of the most active areas in information technology. Ongoing research has revealed a number of intrinsic challenges and problems facing eac...
 
Multi-Space-Mapped SVMs for Multi-class Classification
Found in: Data Mining, IEEE International Conference on
By Bo Liu, Longbing Cao, Philip S. Yu, Chengqi Zhang
Issue Date:December 2008
pp. 911-916
In SVMs-based multiple classification, it is not always possible to find an appropriate kernel function to map all the classes from different distribution functions into a feature space where they are linearly separable from each other. This is even worse ...
 
Behavior Informatics and Analytics: Let Behavior Talk
Found in: Data Mining Workshops, International Conference on
By Longbing Cao
Issue Date:December 2008
pp. 87-96
Behavior is increasingly recognized as a key component in business intelligence and problem-solving. Different from traditional behavior analysis, which mainly focus on implicit behavior and explicit business appearance as a result of business usage and cu...
 
Domain Driven Data Mining (D3M)
Found in: Data Mining Workshops, International Conference on
By Longbing Cao
Issue Date:December 2008
pp. 74-76
In deploying data mining into the real-world business, we have to cater for business scenarios, organizational factors, user preferences and business needs. However, the current data mining algorithms and tools often stop at the delivery of patterns satisf...
 
Mining Exceptional Activity Patterns in Microstructure Data
Found in: Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on
By Yuming Ou, Longbing Cao, Chao Luo, Li Liu
Issue Date:December 2008
pp. 884-887
Market Surveillance plays an important role in maintaining market integrity, transparency and fairness. The existing trading pattern analysis only focuses on interday data which discloses explicit and high-level market dynamics. In the mean time, the exist...
 
Exception Mining on Multiple Time Series in Stock Market
Found in: Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on
By Chao Luo, Yanchang Zhao, Longbing Cao, Yuming Ou, Chengqi Zhang
Issue Date:December 2008
pp. 690-693
This paper presents our research on exception mining on multiple time series data which aims to assist stock market surveillance by identifying market anomalies. Traditional technologies on stock market surveillance have shown their limitations to handle l...
 
F-TRADE 3.0: An Agent-Based Integrated Framework for Data Mining Experiments
Found in: Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on
By Peerapol Moemeng, Longbing Cao, Chengqi Zhang
Issue Date:December 2008
pp. 612-615
Data mining researches focus on algorithms that mine valuable patterns from particular domain. Apart from the theoretical research, experiments take a vast amount of effort to build. In this paper, we propose an integrated framework that utilises a multi-a...
 
EOCS-MCP 2008 Workshop Organization
Found in: 2008 IEEE 32nd International Computer Software and Applications Conference (COMPSAC)
By Longbing Cao,Ruwei Dai,Vladimir Gorodetski
Issue Date:July 2008
pp. 860-861
Provides a listing of current committee members and society officers.
 
Metasynthetic Computing for Solving Open Complex Problems
Found in: Computer Software and Applications Conference, Annual International
By Longbing Cao
Issue Date:August 2008
pp. 896-901
Complex systems, in particular, open complex giant systems have become one of major challenges to many current disciplines such as system sciences, cognitive sciences, intelligence sciences, computer sciences, and information sciences. An appropriate metho...
 
Message from the EOCS-MCP 2008 Workshop Organizers
Found in: 2008 IEEE 32nd International Computer Software and Applications Conference (COMPSAC)
By Longbing Cao,Ruwei Dai,Vladimir Gorodetski
Issue Date:July 2008
pp. 859
No summary available.
   
Developing Actionable Trading Strategies for Trading Agents
Found in: Intelligent Agent Technology, IEEE / WIC / ACM International Conference on
By Longbing Cao, Chao Luo, Chengqi Zhang
Issue Date:November 2007
pp. 72-75
Trading agents are very useful for developing and backtesting quality trading strategies for actions taking in the real world. However, the existing trading agent research mainly focuses on simulation using artificial data and market models. As a result, t...
 
Detecting Turning Points of Trading Price and Return Volatility for Market Surveillance Agents
Found in: Web Intelligence and Intelligent Agent Technology, International Conference on
By Yuming Ou, Longbing Cao, Ting Yu, Chengqi Zhang
Issue Date:November 2007
pp. 491-494
Trading agent concept is very useful for trading strategy design and market mechanism design. In this paper, we introduce the use of trading agent for market surveillance. Market surveillance agents can be developed for market surveillance officers and man...
 
Mining Impact-Targeted Activity Patterns in Imbalanced Data
Found in: IEEE Transactions on Knowledge and Data Engineering
By Longbing Cao, Yanchang Zhao, Chengqi Zhang
Issue Date:August 2008
pp. 1053-1066
Impact-targeted activities are rare but lead to significant impact on the society, e.g., isolated terrorism activities may lead to a disastrous event threatening national security. Similar issues can also be seen in many other areas. Therefore, it is impor...
 
Agent Services-Based Infrastructure for Online Assessment of Trading Strategies
Found in: Intelligent Agent Technology, IEEE / WIC / ACM International Conference on
By Longbing Cao, Jiaqi Wang, Li Lin, Chengqi Zhang
Issue Date:September 2004
pp. 345-348
Traders and researchers in stock marketing often hold some private trading strategies. Evaluation and optimization of their strategies is a great benefit to them before they take any risk in realistic trading. We build an agent services-driven infrastructu...
 
Efficient Mining of Event-Oriented Negative Sequential Rules
Found in: Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on
By Yanchang Zhao, Huaifeng Zhang, Longbing Cao, Chengqi Zhang, Hans Bohlscheid
Issue Date:December 2008
pp. 336-342
Traditional sequential pattern mining deals with positive sequential patterns only, that is, only frequent sequential patterns with the appearance of items are discovered. However, it is often interesting in many applications to find frequent sequential pa...
 
Agent Services-Orinted Architectural Design of Open Complex Agent Systems
Found in: Intelligent Agent Technology, IEEE / WIC / ACM International Conference on
By Longbing Cao, Chengqui Zhang, Jiarui Ni
Issue Date:September 2005
pp. 120-123
<p>Architectural design is a critical phase in building agent-based systems. However, most of existing agent oriented software engineering approaches deliver weak or incomplete supports for the architectural design of distributed and especially Inter...
 
Model the complex dependence structures of financial variables by using canonical vine
Found in: Proceedings of the 21st ACM international conference on Information and knowledge management (CIKM '12)
By Jinyan Li, Longbing Cao, Wei Wei, Xuhui Fan
Issue Date:October 2012
pp. 1382-1391
Financial variables such as asset returns in the massive market contain various hierarchical and horizontal relationships forming complicated dependence structures. Modeling and mining of these structures is challenging due to their own high structural com...
     
Maximum margin clustering on evolutionary data
Found in: Proceedings of the 21st ACM international conference on Information and knowledge management (CIKM '12)
By Lin Zhu, Longbing Cao, Xia Cui, Xuhui Fan, Yew-Soon Ong
Issue Date:October 2012
pp. 625-634
Evolutionary data, such as topic changing blogs and evolving trading behaviors in capital market, is widely seen in business and social applications. The time factor and intrinsic change embedded in evolutionary data greatly challenge evolutionary clusteri...
     
Coupled behavior analysis for capturing coupling relationships in group-based market manipulations
Found in: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12)
By Gang Wei, Longbing Cao, Wei Ding, Wu Ye, Xindong Wu, Yin Song
Issue Date:August 2012
pp. 976-984
In stock markets, an emerging challenge for surveillance is that a group of hidden manipulators collaborate with each other to manipulate the price movement of securities. Recently, the coupled hidden Markov model (CHMM)-based coupled behavior analysis (CB...
     
USpan: an efficient algorithm for mining high utility sequential patterns
Found in: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12)
By Junfu Yin, Longbing Cao, Zhigang Zheng
Issue Date:August 2012
pp. 660-668
Sequential pattern mining plays an important role in many applications, such as bioinformatics and consumer behavior analysis. However, the classic frequency-based framework often leads to many patterns being identified, most of which are not informative e...
     
Coupled nominal similarity in unsupervised learning
Found in: Proceedings of the 20th ACM international conference on Information and knowledge management (CIKM '11)
By Can Wang, Jinjiu Li, Longbing Cao, Mingchun Wang, Wei Wei, Yuming Ou
Issue Date:October 2011
pp. 973-978
The similarity between nominal objects is not straightforward, especially in unsupervised learning. This paper proposes coupled similarity metrics for nominal objects, which consider not only intra-coupled similarity within an attribute (i.e., value freque...
     
e-NSP: efficient negative sequential pattern mining based on identified positive patterns without database rescanning
Found in: Proceedings of the 20th ACM international conference on Information and knowledge management (CIKM '11)
By Chengqi Zhang, Jinjiu Li, Longbing Cao, Wei Wei, Xiangjun Dong, Yanchang Zhao, Yuming Ou, Zhigang Zheng
Issue Date:October 2011
pp. 825-830
Mining Negative Sequential Patterns (NSP) is much more challenging than mining Positive Sequential Patterns (PSP) due to the high computational complexity and huge search space required in calculating Negative Sequential Candidates (NSC). Very few approach...
     
K-farthest-neighbors-based concept boundary determination for support vector data description
Found in: Proceedings of the 19th ACM international conference on Information and knowledge management (CIKM '10)
By Bo Liu, Longbing Cao, Yanshan Xiao
Issue Date:October 2010
pp. 1701-1704
Support vector data description (SVDD) is very useful for one-class classification. However, it incurs high time complexity in handling large scale data. In this paper, we propose a novel and efficient method, named K-Farthest-Neighbors-based Concept Bound...
     
Orientation distance-based discriminative feature extraction for multi-class classification
Found in: Proceedings of the 19th ACM international conference on Information and knowledge management (CIKM '10)
By Bo Liu, Longbing Cao, Philip S. Yu, Yanshan Xiao
Issue Date:October 2010
pp. 909-918
Feature extraction is an effective step in data mining and machine learning. While many feature extraction methods have been proposed for clustering, classification and regression, very limited work has been done on multi-class classification problems. In ...
     
Detecting abnormal coupled sequences and sequence changes in group-based manipulative trading behaviors
Found in: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '10)
By Gang Wei, Longbing Cao, Philip S. Yu, Yuming Ou
Issue Date:July 2010
pp. 85-94
In capital market surveillance, an emerging trend is that a group of hidden manipulators collaborate with each other to manipulate three trading sequences: buy-orders, sell-orders and trades, through carefully arranging their prices, volumes and time, in o...
     
Mining for combined association rules on multiple datasets
Found in: Proceedings of the 2007 international workshop on Domain driven data mining (DDDM '07)
By Chengqi Zhang, Fernando Figueiredo, Huaifeng Zhang, Longbing Cao, Yanchang Zhao
Issue Date:August 2007
pp. 18-23
Many organisations have their digital information stored in a distributed systems structure scheme, be it in different locations, using vertically and horizontally distributed repositories, which brings about an high level of complexity to data mining. Fro...
     
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