An inductive database system based on virtual mining views
Data Mining and Knowledge Discovery, ISSN: 1384-5810, Vol: 24, Issue: 1, Page: 247-287
2012
- 19Citations
- 13Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Inductive databases integrate database querying with database mining. In this article, we present an inductive database system that does not rely on a new data mining query language, but on plain SQL. We propose an intuitive and elegant framework based on virtual mining views, which are relational tables that virtually contain the complete output of data mining algorithms executed over a given data table. We show that several types of patterns and models that are implicitly present in the data, such as itemsets, association rules, and decision trees, can be represented and queried with SQL using a unifying framework. As a proof of concept, we illustrate a complete data mining scenario with SQL queries over the mining views, which is executed in our system. © 2011 The Author(s).
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84856601388&origin=inward; http://dx.doi.org/10.1007/s10618-011-0229-7; http://link.springer.com/10.1007/s10618-011-0229-7; http://link.springer.com/content/pdf/10.1007/s10618-011-0229-7; http://link.springer.com/content/pdf/10.1007/s10618-011-0229-7.pdf; http://link.springer.com/article/10.1007/s10618-011-0229-7/fulltext.html; https://dx.doi.org/10.1007/s10618-011-0229-7; https://link.springer.com/article/10.1007/s10618-011-0229-7; http://www.springerlink.com/index/10.1007/s10618-011-0229-7; http://www.springerlink.com/index/pdf/10.1007/s10618-011-0229-7
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know