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WIREs Data Mining and Knowledge Discovery, 2011
AbstractAs an important data mining and knowledge discovery task,association rule miningsearches for implicit, previously unknown, and potentially useful pieces of information—in the form of rules revealing associative relationships—that are embedded in the data. In general, the association rule mining process comprises two key steps.
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AbstractAs an important data mining and knowledge discovery task,association rule miningsearches for implicit, previously unknown, and potentially useful pieces of information—in the form of rules revealing associative relationships—that are embedded in the data. In general, the association rule mining process comprises two key steps.
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Indexing Uncertain Categorical Data
2007 IEEE 23rd International Conference on Data Engineering, 2007Uncertainty in categorical data is commonplace in many applications, including data cleaning, database integration, and biological annotation. In such domains, the correct value of an attribute is often unknown, but may be selected from a reasonable number of alternatives.
Sarvjeet Singh +4 more
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Classifying univariate uncertain data
Applied Intelligence, 2020In the literature, univariate uncertain data has a quantitative interval for each attribute in each transaction, which is accompanied by a probability density function indicating the probability that each value in the interval exists and appears. To the best of our knowledge, classifying univariate uncertain data has thus far seldom been addressed in ...
Ying-Ho Liu, Huei-Yu Fan
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Preprocessing Uncertain Photovoltaic Data
IEEE Transactions on Sustainable Energy, 2014This letter suggests a method to manage the uncertainty of photovoltaic (PV) data by removing the periodic effect of the annual position of the sun in the sky. The least squares method is applied to determine the low-frequency (annual) periodic component which is predictable in the system operation.
Miao Fan +3 more
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Querying and Cleaning Uncertain Data
2009The management of uncertainty in large databases has recently attracted tremendous research interest. Data uncertainty is inherent in many emerging and important applications, including locationbased services, wireless sensor networks, biometric and biological databases, and data stream applications.
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Analyzing Uncertain Tabular Data
2019It is common practice to spend considerable time refining source data to address issues of data quality before beginning any data analysis. For example, an analyst might impute missing values or detect and fuse duplicate records representing the same real-world entity.
Oliver Kennedy, Boris Glavic
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An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Lynne Penberthy +2 more
exaly
2003
The application of statistical methods to natural language processing has been remarkably successful over the past two decades. But, to deal with recent problems arising in this field, machine learning techniques must be generalized to deal with uncertain data, or datasets whose elements are distributions over sequences, such as weighted automata. This
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The application of statistical methods to natural language processing has been remarkably successful over the past two decades. But, to deal with recent problems arising in this field, machine learning techniques must be generalized to deal with uncertain data, or datasets whose elements are distributions over sequences, such as weighted automata. This
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Uncertain Sampled-data Systems
1996In this chapter we introduce the general sampled-data system configuration considered in this work. The system arrangement is shown in Figure 3.1. The figure shows a continuous time system G in feedback with a discrete time controller Kd through the sample and hold devices defined in (2.2). Also in feedback with G is the block diagonal system diag(Δ1,..
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