Y Shen

Soft Concept Hierarchies to Summarise Data Streams and Highlight Anomalous Changes

A hierarchical approach is natural when managing large volumes of information, from both static (database) and dynamic (datastream) sources. Hierarchies allow progressively finer division into more specific categories, but frequently the categories are fuzzy rather than crisp. In this paper, we use fuzzy formal concept analysis to extract soft hierarchies from data. The hierarchies are used to classify data and to monitor changes over time by means of a fuzzy confidence measure for association analysis. A (simulated) stream of terrorism incident data is used as proof of concept.

Intelligent user search behaviour knowledge discovery

It is important for Web search engine providers to study user behaviour in order to have a better understanding of how customers interact with search engines so that they can improve users overall search experience. However, user behaviour in a search engine is complicated and affected by various factors, e.g. query length, intention/context/time when queries are submitted, etc. It is interesting to find answers to questions such as “whether loyal users are more likely to issue short length queries or medium length queries? If so, is that behaviour linked with high click through rate or is it linked with the users previous search experience?” In this paper we argue that user behaviour should be better analysed from a subjective angle and introduce a granular analysis algorithm to intelligently extract user behaviour knowledge in a human-centric way to answer above questions. We study six variables relating to user behaviour study and demonstrate how fuzzy association rules mining based on mass assignment theory can intelligently analyse user activity patterns in a large scale Web search log data set.

Intelligent Security Data Analysis

In this paper, we examine issues related to the research and applications of computational intelligence techniques in security data analysis. We focus on solve problems that involve incomplete, vague or uncertain information, which is difficult to come to a crisp solution. It is shown how an extended mass assignment framework can be used to extract relations between soft categories. These relations are association rules and are useful when integrating multiple information sources. Experimental results on terrorism incident databases and Web search logs, respectively relating to national security and user behaviour profiling, are demonstrated and discussed in this paper.

IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA 2007), Waikiki, HI, USA

Recent initiatives in defence related information systems have emphasised the need to bring together information from multiple sources and fuse it into a form suitable for decision makers. This paper outlines a four stage system for fusing unstructured and semi-structured text and numerical data by extraction of entities and relations, identification of duplicate entities, organisation into the most appropriate hierarchical categories and determination of relations between fuzzy categories. The novel contribution of this paper is in the final stage of the process, where we determine associations between fuzzy categories and identify strong and/or unusual levels of association as well as changes over time. A demonstrator application shows how information on terrorist incidents from multiple sources can be integrated and monitored.