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This paper proposes a novel algorithm to discover overlapping communities.
We design an efficient algorithm to discover all frequent maximal homomorphic tree patterns.
We proposed a fully distributed (no external information is required) algorithm to discover the satisfaction algorithm.
We then propose the use of a genetic algorithm to discover sets of parameters that guarantee good floorplan quality.
An algorithm to discover the suffix set S and build the PST using a given training sequence of length m is explained in the papers [6,28].
MFS-E-EDC is done by integrating constraints (s_{0}) and (C_{11}) into dEclat algorithm to discover only frequent itemsets satisfying two these constraints.
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We developed several machine-learning algorithms to discover the meaning of each adjective from the robot's sensory data.
To discover topics, we converted each sentence of the document into a transaction format, and employed association rule mining algorithms to discover frequent patterns from the documents.
By representing videos as a histogram of iconic occurrences, we can develop efficient algorithms to discover inter-sequence relationships within a video collection.
Many experiments show that a large in scale and complicated in structure smart phone software can be tested using our innovative algorithms to discover more deep hidden logical errors efficiently and effectively.
Community detection is fundamental for revealing the structure-functionality relationship in complex networks, which involves two issues the quantitative function for community as well as algorithms to discover communities.
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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