Why Are All Points in [0,1] Cluster Points of the Interval (0,1)?

In summary, the conversation discusses the definition of a cluster point and how it applies to the open interval (0,1) in the metric space R^1. It is concluded that the set of cluster points of A in R^1 is [0,1]. The conversation also touches on the difficulty in proving this and a warning about the rotavirus.
  • #1
happyg1
308
0
cluster point confusion...

Fog Fog Fog...
Ok,Here's the question:
Let A denote the open interval (0,1).Show that the set of Cluster points of A in [tex]R^1[/tex] is [0,1].
Our textbook sez that [tex]R^1[/tex] is defined as the absolute value metric, i.e. [tex]\rho[/tex](x,y)=|x-y|
OK
So I know (and have proven) that (0,1) is uncountable and that there are infinitely many points between any 2 points in (0,1). It is easy for me to understand that the definition of a cluster point
we which we have as:
"let M,[tex]\rho[/tex] be a metric space and suppose A [tex]\subset[/tex]M. The point a [tex]\in[/tex]M is called a cluster point of A in M if, for every h>0, there exists a point x[tex]\in[/tex]A such that 0,[tex]\rho[/tex](x,a)<h."
is fulfilled...no matter what h I pick, I will always be able to find some x...
I just don't know where to start to write it down in a fashion that my prof. would accept. He's VERY picky and I'm VERY tired.
Give me some nudges, please...
BTW...If ANY of Ya'll have kids, WATCH OUT for the rotavirus...wash your hands A LOT. Both of my littles have been hospitalized for dehydration (it's gnarly stomach flu...)
Thanks,
CC
 
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  • #2
It's just the closure.

If you need to use the definition as given, and you already know that the answer is [0,1] just do it: if x is not in [0,1] show it is not a cluster point, and if x is in [0,1] show it is a cluster point, obviously all points in (0,1) are cluster points, so it only remains to show that 1 and 0 are cluster points which is exactly as hard as knowing that 1/n tends to 0
 

FAQ: Why Are All Points in [0,1] Cluster Points of the Interval (0,1)?

What is cluster point confusion?

Cluster point confusion is a phenomenon in which multiple data points in a dataset are very close to each other and cannot be easily distinguished. This can lead to difficulties in accurately identifying and analyzing trends and patterns in the data.

What causes cluster point confusion?

Cluster point confusion can be caused by a variety of factors, such as errors in data collection, outliers in the dataset, or limitations in the data analysis methods being used. It can also occur naturally in complex datasets with overlapping data points.

How can cluster point confusion be addressed?

To address cluster point confusion, it is important to carefully review and clean the dataset before analysis. This can involve removing outliers, identifying and addressing errors in the data, and using appropriate data analysis techniques that can handle overlapping data points.

What are the consequences of not addressing cluster point confusion?

If cluster point confusion is not addressed, it can lead to inaccurate insights and conclusions being drawn from the data. This can result in poor decision-making and incorrect predictions, which can have significant consequences in various fields such as healthcare, finance, and business.

Are there any tools or techniques specifically designed to handle cluster point confusion?

Yes, there are various tools and techniques that can help in handling cluster point confusion, such as clustering algorithms, density-based methods, and visualization techniques. It is important to carefully select and use the most appropriate method for a specific dataset to effectively address cluster point confusion.

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