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Each instance is a set of attribute values described by a vector, X = (x1, x2,…, xn).
Assume we have a graph (G = (U, V, E)), where G is a bipartite graph consisting of parts U and V, and edges E. A set of tuples can be projected to U and a set of attribute values to V, and the existence of a tuple containing a set of attribute values to an edge in E. A clique in a bipartite graph is equivalent of a set of attribute values that is common in a set of tuples.
Such queries retrieve a number of tuples, or objects, from the database, which can be generally viewed as a set of attribute values represented as data points in a multi-dimensional data space.
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A document can be a simple or complex value, a set of attribute-value pairs, which can comprise simple values, lists, and even nested sub documents.
Similarly, the problem of finding a common set of attribute values among a set of tuples can be reduced to a bipartite clique problem as follows.
Feature-based machine learning algorithms require the instances to have a fixed set of attribute values from a feature space.
The focus of this study is on preserving the confidentiality of outsourced relational data, i.e., data that contains records consisting of a fixed set of attribute values.
Let B be a fixed finite set (the set of attribute values).
CO assumes that each node has a set of attribute name-value pairs.
Moreover, as we do not fix the set of attribute values, i.e. the state space might be infinite, it is impossible to define each potential reaction.
Let S i, j be the normalized similarity between two sets of attribute values X i and X j of datasets i and j.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com