Explain a Weblog Community

Ochoa, Alberto; Zamarrón, Antonio; González, S.; Castro, Arnulfo; Padilla, Nahitt

doi:10.1007/978-3-540-74972-1_62

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…By analyzing what kind of music is someone listening, a system can deduce the songs, the singers and the genders the person prefers, and by using this information recommending additional songs and artists, to get the person in touch with people of similar interests. We made an approach on this direction with a system that allows users to view individual and historical group listening lists and define with this information new listening lists (Ochoa, 2007). These systems learn of the user preferences based on the listened music, after songs are selected to be played on a shared physical environment, based on the preferences of the whole participants.…”

Section: Conclusion and The Future Researchmentioning

confidence: 99%

Data Mining in Medical and Biological Research

Γιαννοπούλου¹

2008

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Data mining is the research area involving powerful processes and tools that allow an effective analysis and exploration of usually large amounts of data. In particular, data mining techniques have found application in numerous different scientific fields with the aim of discovering previously unknown patterns and correlations, as well as predicting trends and behaviors.In the meantime, during the current century of biomedical sciences, biology and medicine have undergone tremendous advances in their technologies and therefore have generated huge amounts of biomedical information. This book intends to bring together the most recent advances and applications of data mining research in the promising areas of medicine and biology from around the world. Seventeen chapters, twelve related to medical research and five focused on the biological domain, describe interesting applications, motivating progress and worthwhile results.Chapter 1 presents a novel concept of improving the classification accuracy for common learning algorithms by uncovering the potential relevance of features through multivariate interdependent attribute preprocessing methods.Chapter 2 introduces a hybrid approach, combining discrete wavelet transform and neural networks, for the classification of a complex dataset such as an electroencephalography time series and claims that a high degree of classification accuracy can be achieved.Chapter 3 explores the possibility that DNA viruses play an important role in development of breast tumors, using artificial neural networks and agglomerative hierarchical clustering techniques.Chapter 4 investigates the research hypothesis of developing certain domain ontology from discovered rules. To practically examine this assumption, the proposed approach integrates different data mining techniques to assist in developing a set of representative consensual concepts of the underlying domain.Chapter 5 describes the process of medical knowledge acquisition and outlines the requirements for producing knowledge that can be trusted and applied in a clinical setting. This work demonstrates that the individual needs of medical professionals can be addressed and that data mining can be a valuable tool in the diagnostic and decision making toolkit.Chapter 6 provides an overview of the various technologies in a radiology department that allow data mining, and describes case-examples utilizing data mining technologies.Chapter 7 demonstrates the results of a nationwide study that applied data and text mining techniques to medical near-miss case data related to medicines and medical equipments. This study also analyzed the causal relationship of occurrences of the near-miss cases and the opinions on the counter measures against them. VI Chapter 8 presents a complex clinical problem that has been addressed using particle swarm optimization, a suitable approach for data mining subject to a rule base which defines the quality of rules and constancy with previous observations. Chapter 9 focuses on the description of a model for mo...

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Section: Conclusion and The Future Researchmentioning

confidence: 99%

Data Mining in Medical and Biological Research

Γιαννοπούλου¹

2008

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Social Data Mining to Improve Bioinspired Intelligent Systems

Ochoa¹,

González²,

Castro³

et al. 2008

Data Mining in Medical and Biological Research

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Social data mining systems enable people to share opinions and obtain a benefit from each other's experience. These systems do this by mining and redistributing information from computational records of social activity such as Usenet messages, system usage history, We report here on social data mining applications, systems that mine information from the structure and content of web pages and provide an exploratory information workspace interface. We carried out experiments that yielded positive answers to both evaluation questions. First, a number of automatically computable features about web sites do a good job of predicting expert quality judgments about sites. Second, compared to popular web search interfaces, the Topic Shop interface to this information lets users select significantly higher quality sites, in less time and with less effort, and to organize the sites they select into personally meaningful collections quickly and easily. We conclude by discussing how our results may be applied and considering how they touch on general issues concerning quality, expertise, and consensus The motivation for the social data mining approach goes back at least to Vannevar Bush's As We May Think essay. Bush envisioned scholars blazing trails through repositories of information and realized that these trails subsequently could be followed by others. Everyone could walk in the footsteps of the masters. In our work, we have formulated a similar intuition using the metaphor of a path through the woods. However, this metaphor highlights the role of collective effort, rather than the individual. A path results from the decisions of many individuals, united only by where they choose to walk, yet still reflects a rough notion of what the walkers find to be a good path. The path both reflects history of use and serves as a resource for future users. Social data mining approaches seek analogous situations in the computational world.Researchers look for situations where groups of people are producing computational records (such as documents, Usenet messages, or web sites and links) as part of their normal activity. Potentially useful information implicit in these records is identified, computational techniques to harvest and aggregate the information are invented, and visualization techniques to present the results are designed. Thus, computation discovers and makes explicit the "paths through the woods" created by particular user communities. And, unlike ratings-based collaborative filtering systems (Resnick et al., 1994), social data mining systems do not require users to engage in any new activity; rather, they seek to exploit user preference information implicit in records of existing activity. The "history-enriched digital objects" line of work (Hill et al., 1992) was a seminal effort in this approach. It began from the observation that objects in the real world accumulate wear over the history of their use, and that this wear -such as the path through the woods or the dog-eared pages in a paperback book or the smudges on...

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Explain a Weblog Community

Cited by 2 publications

References 4 publications

Data Mining in Medical and Biological Research

Data Mining in Medical and Biological Research

Social Data Mining to Improve Bioinspired Intelligent Systems

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