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In general, data can be categorized into three types: structured, semi-structured, and unstructured data.
Impedance based fault location from one end data can be categorized based on the source at one end and source at two ends.
Methods and algorithms for social big data can be categorized into network analytics, community detection algorithms, text analytics, information diffusion models and methods, and information fusion [18].
The results show that the households within the CHFS data can be categorized into two classes, and that households within each class rank the importance of socioeconomic variables in significantly different ways.
A key advantage of the LRTS is that it can be computed on any CNP data, whether or not that data can be categorized.
Computational methods for reconstructing GINs from expression data can be categorized into four groups: gene-pair based, single-gene targeted, gene-module based, and integrative methods [ 1].
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These types of data items can be categorized as query data items, and an archetype may have multiple query data items to facilitate the data query.
Methods and algorithms for social media data analysis can be categorized into network analytics, community detection algorithms, text analytics, information diffusion models and methods, and information fusion [23].
From a broader perspective, the big data platforms can be categorized into the following two types of scaling: Horizontal Scaling: Horizontal scaling involves distributing the workload across many servers which may be even commodity machines.
These approaches encompass a number of techniques that focus on the discovery of patterns and trends in data and can be categorized as being either 'supervised' or 'unsupervised' [ 14].
Data quality factors can be categorized as data completeness (are all the data present?), data reliability (are data recorded in the same way across practices and over time?) and data validity (are the data correct?) [ 14, 15].
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