Carlos Rojas

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Web usage mining has recently attracted attention as a viable framework for extracting useful access pattern information, such as user profiles, from massive amounts of Web log data for the purpose of Web site personalization and organization. These efforts have relied mainly on clustering or association rule discovery as the enabling data mining(More)
Artificial Immune System (AIS) models offer a promising approach to data analysis and pattern recognition. However, in order to achieve a desired learning capability (for example detecting all clusters in a dat set), current models require the storage and manipulation of a large network of B Cells (with a number often exceeding the number of data points in(More)
The expanding and dynamic nature of the Web poses enormous challenges to most data mining techniques that try to extract patterns from Web data, such as Web usage and Web content. While scalable data mining methods are expected to cope with the size challenge, coping with evolving trends in noisy data in a continuous fashion, and without any unnecessary(More)
BACKGROUND Delirium is an important problem especially in older medical inpatients. OBJECTIVE The authors asked whether delirium and its duration are associated with higher mortality in a 3-month follow-up period. METHOD In this prospective cohort study, inpatients age 65 and older were assessed every 48 hours with the Confusion Assessment Method. (More)
In addition to its ever-expanding size and lack of structure, the World Wide Web has not been responsive to user preferences and interests. Personalization deals with tailoring a user's interaction with the Web information space based on information about him/her. Mass profiling is based on general trends of usage patterns (thus protecting privacy) compiled(More)
In this paper, we study the behavior of collaborative filtering based recommendations under evolving user profile scenarios. We propose a systematic validation methodology that allows for simulating various controlled user profile evolution scenarios and validating the studied recommendation strategies. Through the presented work, we observe the effect of(More)
While scalable data mining methods are expected to cope with massive Web data, coping with evolving trends in noisy data in a continuous fashion, and without any unnecessary stoppages and reconfigurations is still an open challenge. This dynamic and single pass setting can be cast within the framework of mining evolving data streams. In this paper, we(More)
Data on the Web is noisy, huge, and dynamic. This poses enormous challenges to most data mining techniques that try to extract patterns from this data. While scalable data mining methods are expected to cope with the size challenge, coping with evolving trends in noisy data in a continuous fashion, and without any unnecessary stoppages and reconfigu-rations(More)
In stream data mining it is important to use the most recent data to cope with the evolving nature of the underlying patterns. Simply keeping the most recent records offers no flexibility about which data is kept, and does not exploit even minimal redundancies in the data (a first step towards pattern discovery). This paper focuses in how to construct and(More)