2,648 research outputs found
Statistical strategies for pruning all the uninteresting association rules
We propose a general framework to describe formally the
problem of capturing the intensity of implication for
association rules through statistical metrics.
In this framework we present properties that influence the
interestingness of a rule, analyze the conditions that
lead a measure to perform a perfect prune at a time,
and define a final proper order to sort the surviving
rules. We will discuss why none of the currently employed
measures can capture objective interestingness, and
just the combination of some of them, in a multi-step fashion,
can be reliable. In contrast, we propose a new simple modification
of the Pearson coefficient that will meet all the necessary
requirements. We statistically infer the convenient cut-off
threshold for this new metric by empirically describing its
distribution function through simulation. Final experiments
serve to show the ability of our proposal.Postprint (published version
Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art
Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover
A survey of temporal knowledge discovery paradigms and methods
With the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behavior associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining
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