Trading agent concept is very useful for tradingstrategy design and market mechanism design. In thispaper, we introduce the use of trading agent for marketsurveillance. Market surveillance agents can bedeveloped for market surveillance officers andmanagement teams to present them alerts and...

Trading agents are very useful for developing andback-testing quality trading strategies to support smarttrading actions in the market. However, the existingtrading agent research mainly focuses on simple andsimulated strategies. As a result, there exists a big gapbetween academia and business...

In a foreign currency exchange market, there are highdensitydata streams. The present approaches forvisualization of this type of data cannot show us a figurewith targeted both local details and global trendinformation. In this paper, on the basis of features andattributes of foreign currency...

The aims and objectives of data mining is to discover actionable knowledge of maininterest to real user needs, which is one of Grand Challenges in KDD. Most extant data miningis a data-driven trial-an-error process. Patterns discovered via predefined models in the aboveprocess are often of...

Activity data accumulated in real life, e.g. in terrorist activities andfraudulent customer contacts, presents special structural and semanticcomplexities. However, it may lead to or be associated with significant businessimpacts. For instance, a series of terrorist activities may trigger a...

Market Surveillance plays important mechanism roles in constructing market models. From data analysis perspective, we view it valuable for smart trading in designing legal and profitable trading strategies and smart regulation in maintaining market integrity, transparency and fairness. The...

Stock trading plays an important role for supporting profitable stock investment. In particular, more and more data mining-based technical trading rules have been developed and used in stock trading systems to assist investors with their smart trading decisions. However, many mined trading rules...

From a data mining perspective, sequence classification is to build a classifier using frequent sequential patterns. However, mining for a complete set of sequential patterns on a large dataset can be extremely time-consuming and the large number of patterns discovered also makes the pattern...

Activity data accumulated in real life, such as terrorist activities and governmental customer contacts, present special structural and semantic complexities. Activity data may lead to or be associated with significant business impacts, and result in important actions and decision making leading...

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 satisfying expected technical interestingness....