point aggregation

USAGE SUMMARY

The phrase "point aggregation" is correct and usable in written English.
It can be used in contexts related to data analysis, statistics, or any field where data points are combined or summarized. Example: "The point aggregation method allowed us to visualize the data trends more effectively."

✓ Grammatically correct

Science

News & Media

Formal & Business

Human-verified examples from authoritative sources

Exact Expressions

6 human-written examples

However at some point aggregation is also not enough.

TechCrunch

We use these data to explore the three open questions of Hounslow et al. (2013) and show how to average underlying point aggregation kinetics over a whole vessel and how to extract point data from average data.

Chemical Engineering Science

The 20% densest point aggregation is within the triangle (2, −2), (2, −8), and (0, −3).

Plosone

Above the cloud point, aggregation of detergent micelles results in a two-phase system consisting of an aqueous phase and a detergent-rich phase.

Bioscience Reports

It was found that predicted pI, averaged over the protein structures in a subcellular compartment, tends to lie away from the subcellular pH, consistent with a role in mitigating against isoelectric point aggregation.

BMC Biology

The heat generation in our quite concentrated nanoparticle system was, however, sufficiently efficient to heat the entire bulk solution to the LCST within 5 min, at which point aggregation was observed; strong aggregation and precipitation was observed after 10 min of magnetic heating.

ACS Applied Materials & Interfaces

Human-verified similar examples from authoritative sources

Similar Expressions

54 human-written examples

Colley argues that this turning point of aggregation prevented both civil war and invasion from abroad.

The Guardian - Books

"And at some point, the aggregation of all those individual tipping points having been reached, it will be, in effect, the country will have tipped".

The New York Times - Magazine

He meant relying on third party sources or press releases from various entities… That's not exactly a consolation to me… At some point, the aggregation ecosystem might simply run out of original news to feed — or prey — on.

The Guardian - Tech

And automative dealers – that's the natural point of aggregation of most services," says Tobey.

TechCrunch

The nodes form a cluster and elect a leader which becomes the point of aggregation.

EURASIP Journal on Wireless Communications and Networking

Expert writing Tips

Best practice

When using "point aggregation" in technical writing, clearly define the criteria for grouping or combining data points to ensure clarity and reproducibility.

Common error

Avoid using "point aggregation" without specifying the method or criteria used for aggregation, as this can lead to ambiguity and misinterpretation. Always clarify what characteristics define a cluster or aggregate.

Linguistic Context

The phrase "point aggregation" functions as a noun phrase, typically serving as a subject or object in a sentence. As Ludwig AI highlights, it refers to the process or result of combining individual data points into a collective representation.

Expression frequency: Rare

Frequent in

Science

60%

News & Media

20%

Formal & Business

20%

Less common in

Social Media

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, the phrase "point aggregation" is a grammatically sound term used to describe the process of combining data points, often in scientific or technical contexts. While not a highly frequent term, as Ludwig highlights, it is employed in scenarios where consolidating data is essential for analysis or simplification. Related phrases include "data point clustering" and "data consolidation". When using this term, ensure clarity by specifying the aggregation criteria. The frequency of use is rare, but the term appears in Science, News & Media and Formal & Business sources.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

aggregate data points

Reverses the order of words, which slightly changes the emphasis.

data point clustering

Focuses on the grouping of data points, emphasizing the spatial aspect of aggregation.

data consolidation

Highlights the merging of data into a unified form, emphasizing simplification.

data point accumulation

Stresses the process of gradually gathering data points.

information gathering

Emphasizes the collection of information, shifting focus from the aggregation process itself.

data summarization

Highlights the process of condensing data into a more concise form.

centralized data collection

Focuses on collecting data in one location, rather than the process of aggregating.

integrated data analysis

Highlights the analytical aspect of dealing with combined data.

clustered data

Focuses on the state of the data being grouped rather than the act of grouping.

integrated data

Focuses on the result of integrated data rather than how that data was aggregated.

FAQs

How is "point aggregation" used in data analysis?

"Point aggregation" is used to combine individual data points into meaningful clusters or summaries, often to identify trends, patterns, or anomalies. For instance, "data point clustering" is a form of "point aggregation" used in spatial analysis.

What is the difference between "point aggregation" and data consolidation?

"Point aggregation" focuses on combining individual data points based on proximity or similarity, while "data consolidation" emphasizes merging data from various sources into a unified format. So "point aggregation" is a specific form of data consolidation.

What are some alternatives to using the phrase "point aggregation"?

Depending on the context, alternatives to "point aggregation" include "data point clustering", "data consolidation", or "data summarization".

In what fields is "point aggregation" commonly used?

"Point aggregation" is commonly used in fields such as statistics, data mining, geographical information systems (GIS), and network analysis to understand spatial distributions, identify clusters, and simplify complex datasets.