We do data normalization when seeking for relations. Some people do this methods, unfortunately, in experimental designs, which is not correct except if the variable is a transformed one, and all the data needs the same normalization method, such as pH in sum agricultural studies. Normalization in experimental designs are meaningless because we can't compare the mean of, for instance, a treatment with the mean of another treatment logarithmically normalized. In regression and multivariate analysis which the relationships are of interest, however, we can do the normalization to reach a linear, more robust relationship. Commonly when the relationship between two dataset is non-linear we transform data to reach a linear relationship. Here, normalization doesn't mean normalizing data, it means normalizing residuals by transforming data. So normalization of data implies to normalize residuals using the methods of transformation.

Notice that do not confuse normalization with standardization (e.g. Z-score).

We do data normalization when seeking for relations. Some people do this methods, unfortunately, in experimental designs, which is not correct except if the variable is a transformed one, and all the data needs the same normalization method, such as pH in sum agricultural studies. Normalization in experimental designs are meaningless because we can't compare the mean of, for instance, a treatment with the mean of another treatment logarithmically normalized. In regression and multivariate analysis which the relationships are of interest, however, we can do the normalization to reach a linear, more robust relationship. Commonly when the relationship between two dataset is non-linear we transform data to reach a linear relationship. Here, normalization doesn't mean normalizing data, it means normalizing residuals by transforming data. So normalization of data implies to normalize residuals using the methods of transformation.

Notice that do not confuse normalization with standardization (e.g. Z-score).

Of you're counting the frequency of occurrence of the same phenomena in two different population with different size and you want to compare them, you have to normalize both, because otherwise you do not now how big the influence of your phenomena is in relation to the total number of cases. Thus, normalization is needed, when comparing populations/phenomena of different size but with the same origin.

In data mining, we can consisted the four main steps:

1. Smoothing data

2. Generalization of data

3. Normalization of data

4. Construction of new characteristics

We can easier construct the new characteristics after the normalization process.

In ANN and other data mining approaches we need to normalize the inputs, otherwise the network will be ill-conditioned. In essence, normalization is done to have the same range of values for each of the inputs to the ANN model. This can guarantee stable convergence of weight and biases.

In distance-based classification, for instance, we need to normalize eache feature value of a feature vector in order to not get conditioned by features with wider range of possible values when computing distances.

If a feature has range in [-1,1] and another feature has range in [-100; 100], a small variation in the feature 2 is probably more influencing than a big variation in feature 1 when computing the distance of two feature vectors.

Also the standardization or z-score is a kind of normalization.

Http://sqlmag.com/database-performance-tuning/sql-design-why-you-need-database-normalization

if I understand correctly, referring to the standardization is done, and this is generally used for the results to be contextualized group analysis.

Dear Farid,

Thank you for the link, but the question is about data normalization (Statistics), not database normalization.

I use data [(x-mean)/sd] normalization whenever differences in variable ranges could potentially affect negatively to the performance of my algorithm. This is the case of PCA, regression or simple correlation analysis for example.

I use [x/max] when I'm just interested in some internal structure of the samples and not in the absolute differences between samples. This might be the case of peak detection in spectra for samples in which the strength of the signal which I'm seeking changes from sample to sample.

Finally I use [x-mean] normalization when some samples could be potentially using just a part of a bigger scale. This is the case of ratings for movies for example, in which by some user tend to give more positive ratings than others.

Could anyone in this group provide a reference (book preferred) on data normalization? Thanks

I have a question. We are performing an experiment to compare reaction time of phosphoric acid with calcite and with dolomite. One of my teachers asked me to normalise the data used as an input to this experiment, I suppose this means normalising the ionic radii of the two elements, because it is the main element which makes dolomite react more slowly than calcite. The ionic radius of calcite is 99 pm and dolomite is 72 pm. How shoud I do this and is it  really necessary? The output of the experiment is pressure of CO2 produced in the reaction.

Please  read the document from the link about normalization and stanrdization

https://docs.google.com/a/koyauniversity.org/document/d/1x0A1nUz1WWtMCZb5oVzF0SVMY7a_58KQulqQVT8LaVA/edit#

Thank you Ali for your contribution...

In Neural network we need normalizing data (features) when they have different ranges, for example one of them ranges from (1000-30000) while another feature ranges from (0.01 - 0.99). We cast both of them in one unified range for example (-1 to +1) or (0 to 1).. why we do that ? Two reasons, first to eliminate the influence of one factor over another (i.e. to give features equal chances), second reason is that the gradient descent with momentom GDM algorithm which is used for backpropagation converges faster with normalized data than with un-normalized data.   So, if you have different features have same range of data then you don't need normalization.

Read the link I provided, it contains the equations required for normalization for both [0,1] and [-1, +1] ranges.

Standardization is changing data in such away that the new set has mean=0 and standard deviation =1. This kind of scaling is useful when the set of the data contains outliers (anomalies), because it has no boundaries like normalization.  

https://docs.google.com/a/koyauniversity.org/document/d/1x0A1nUz1WWtMCZb5oVzF0SVMY7a_58KQulqQVT8LaVA/edit

I suppose I don't need data normalization at all because I don't intend to compare different experiments just do one (sorry - two - but in the same range) and hope to resolve mathematical model that lies behind it. I just have to gather more data about kinetics of reactions of calcite with phosphoric acid and dolomite with phosphoric acid. The problem is, there is not much about it in the web. I have looked for order of reaction and rate reaction constant and found nothing about these particular reactions.

I have a question related to this one. I understand that the data need to be normalized when do regression analysis. But how about correlation ? Since correlation is different as regression, do we still do normalization before correlation analysis ? Thanks! 

yes before using correlation we use normalization to keep our data contrast in one level,otherwise due to contrast variations, results of correlation may not be accurate,can be distracted.This is all in terms of pattern matching,image processing.

Concept of normalization is same in both data minning and image processing.Scope is different.

@Juan Luis Herrera Cortijo

Dear Juan

Can you elaborate more. Do we need to apply normalization before finding correlation coefficient.

Data Normalization and Standardization

When approaching data for modeling, some standard procedures should be used to prepare the data for modeling:

First the data should be filtered, and any outliers removed from the data (watch for a future post on how to scrub your raw data removing onlylegitimate outliers).

The data should be normalized or standardized to bring all of the variables into proportion with one another.  For example, if one variable is 100 times larger than another (on average), then your model may be better behaved if you normalize/standardize the two variables to be approximately equivalent.  Technically though, whether normalized/standardized, the coefficients associated with each variable will scale appropriately to adjust for the disparity in the variable sizes.  However, if normalized/standardized, then the coefficients will reflect meaningful relative activity between each variable (i.e., a positive coefficient will mean that the variable acts positively towards the objective function, and vice versa, plus a large coefficient versus a small coefficient will reflect the degree to which that variable influences the objective function.  Whereas the coefficients from un-normalized/un-standardized data will reflect the positive/negative contribution towards the objective function, but will bill much more difficult to interpret in terms of their relative impact on the objective function.

Non-numeric qualitative data should be converted to numeric quantitative data, and normalized/standardized.  For example, if a survey question asked an interviewee to select where the economy will be for the next six months (i.e., deep recession, moderate recession, mild recession, neutral, mild recovery, moderate recovery, or strong recovery), these can be converted to numerical values of 1 through 7, and thus quantified for the model.

So when we speak of data normalization and data standardization, what is meant?    To normalize data, traditionally this means to fit the data within unity (1), so all data values will take on a value of 0 to 1.  Since some models collapse at the value of zero, sometimes an arbitrary range of say 0.1 to 0.9 is chosen instead, but for this post I will assume a unity-based normalization.   The following equation is what should be used to implement a unity-based normalization:

@Qiumei Zhou

If the features you are correlating are fairly different in units (mg vs kg, nm vs C, kJ vs m3, etc) then it is better to perform normalization before you start correlation.

Some other cases like when you correlate different types of features, like comparing categoric vs numeric, or date/time vs numeric, then you MUST normalize data before correlating, or search alternative methods of relations' mining.

I need help! I ran 452 samples for the same experiment, but I did them separately by taking a few each time (I did it more than 5 times).  Now I have to combine the data and make a scatter plot.  My advisor said I need to "normalize" the data since it was from separate rans.  I normalized it according to stuff I rea

I need help! I ran 452 samples for the same experiment, but I did them separately by taking a few each time (I did it more than 5 times). Now I have to combine the data and make a scatter plot. My advisor said I need to "normalize" the data since it was from separate rans. I normalized it according to stuff I rea

if there is no normalization procedure, then different kinds of features have different ranges, so effect of a certain kind of features may be despised.

Absolutely, NEVER. check out our new paper

Article TO DETERMINE SKEWNESS, MEAN AND DEVIATION WITH A NEW APPROAC...

Please I need a help! what distance is best used  between multiset s and how do we weight a data which are which is a string?

Dear All,

Talking of normalization of datasets. I would like to know if there's any common equation that covers almost all types of datasets for normalization. Or each form of data needs to apply a certain equation during normalization??

Dears,

I have the following case, I am not sure witch method to use:

Year math english City GPA

2000 90 out of 100 100 out of 150 City1 80/100

2001 88 out of 120 80 out of 100 City2 90/100

.

.

.

as you can see the math mark some time out of 100 and some time out of 120, the same as English and other subjects, and from year to year and from City to city this also changes.

which method appropriate for this case???

JJihad

You need to pay attention to which particular problem you want to apply normalization.

In some cases it is useful, in other it isn't.

I usually normalize data everytime a large difference in the ranges of different datasets can create ill-conditioning in the analysis that I have to do. Examples are fitting a surrogate model that requires the solution of an optimization problem and it is difficult to know in advance the ranges of model parameters.

Another situation where normalization is useful is the during the solution of an optimization problem.

On the other hand, I consider normalization not appropriate in all the situations when the distance between data is important.

If you are running a clustering algorithm, normalizing the data may change the final result.

It is difficult to find a general answer, but always normalizing the data before knowing the analysis to perform is not a good practice.

I know the matlab tool DACE of Kriging has its normalization operation.

thank you all's

consult this:

https://docs.google.com/a/koyauniversity.org/document/d/1x0A1nUz1WWtMCZb5oVzF0SVMY7a_58KQulqQVT8LaVA/edit#

good luck

data normalization is used to restrict the data to certain range

obtained by the formula

x in = x – xmin / xmax – x min

The easiest way to do this is to put the data in the same domain when it's not in a domain.

I have taken few values in micrometer and some in terms of number.....should I standardize the data before PCA or not

All the best for your project dear

Please use link : https://www.ringlead.com › blog › 3-reasons-to-normalize-your-data

Dr Kamath Madhusudhana