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This paper investigates, through the use of data mining techniques, patterns in HIV/AIDS patient data. These patterns can be used for better management of the disease and more appropriate targeting of resources.

A total of 250,000 anonymised records from HIV/AIDS patients in Thailand were imported into a database. IBM's Intelligent Miner was used for clustering and association rule discovery.

Clustering highlighted groups of patients with common characteristics and also errors in data. Association rules identified associations that were not expected in the data and were different from traditional reporting mechanisms utilised by medical practitioners. It also allowed the identification of symptoms that co‐exist or are precursors of other symptoms.

Identification of symptoms that are precursors of other symptoms can allow the targeting of the former so that the later symptoms can be avoided. This study shows that providing a pragmatic and targeted approach to the management of resources available for HIV/AIDS treatment can provide a much better service, while at the same time reducing the expense of that service. This study can also be used as a means of implementing a quality monitoring system to target available resources.

Trajectory is the path a moving object takes in space. To understand the trajectory movement patters, data mining is used. However, pattern analysis needs semantics to be understood. Therefore, the purpose of this paper is to enrich trajectories with semantic annotations, such as the name of the location where the trajectory has stopped, so that the paper is able to attain quality decisions.

An experiment was conducted to explain that the use of raw trajectories alone is not enough for the decision-making process and detailed pattern extraction.

The findings of the paper indicates that some fundamental patterns and knowledge discovery is only obtainable by understanding the semantics underlying the position of each point.

The unavailability of data are a limitation of the paper, which would limit its generalizability. Additionally, the lack of availability of tools for automatically adding semantics to clusters posed as a limitation of the paper.

The paper encourages governments as well as businesses to analyze movement data using data mining techniques, in light of the surrounding semantics. This will allow, for example, solving traffic congestions, since by understanding the movement patterns, the traffic authority could make decisions in order to avoid such congestions. Moreover, it could also help tourism authorities, at national levels, to know tourist movement patterns and support these patterns with the required logistical support. Additionally, for businesses, mobile operators could dynamically enhance their services, voice and data, by knowing the semantically enriched patterns of movement.

The paper contributes to the already rare literature on trajectory mining, enhanced with semantics. Mainstream literature focusses on either trajectory mining or semantics, therefore the paper claims that the approach is novel and is needed as well. By integrating mining outcomes with semantic annotation, the paper contributes to the body of knowledge and introduces, with lab evidence, the new approach.