I have asked this question also on "Cross Validated" forum, but with no answer so far, so I am trying also here:
I would like to compute similarity matrix (which I will further use for clustering purposes) from my data (failure data from automotive company). The data consist of these variables:
START DATE + TIME (dd/mm/yyyy hh/mm/ss), DURATION (in seconds), DAY OF THE WEEK (mon,tue,...), WORKING TEAM (1,2,3), LOCALIZATION (1,2,3,...,20), FAILURE TYPE
From this, it is clear, that there are continuous and categorical data. What method would you suggest to calculate similarities between failure types? I think I can not use Euclidean distance, or Gowe's similarity. Thank you in advance.
No, you need an ad hoc function that represents your knowledge about what the data means in the real world. Presumably it will be mainly applying a weight to a continuous difference, and a 2D simple matrix for the discrete categorical variables. But don't rule our censoring of extreme values or fuzzification.
Related
Absolute beginner here. I'm trying to use a neural network to predict price of a product that's being shipped while using temperature, deaths during a pandemic, rain volume, and a column of 0 and 1's (dummy variable).
So imagine that I have a dataset given those values as well as column giving me time in a year/week format.
I started reading Rob Hyndman's forecasting book but I haven't yet seen anything that can help me. One idea that I have is to make a variable that's going to take out each column of the dataframe and make it into a time series. For example, for rain, I can do something like
rain <- df$rain_inches cost<-mainset3 %>% select(approx_cost) raintimeseries <-ts(rain, frequency=52, start=c(2015,1), end=c(2021,5))
I would the same for the other regressors.
I want to use neural networks on each of the regressors to predict cost and then put them all together.
Ideally I'm thinking it would be a good idea to train on say, 3/4 ths of the time series data and test on the remain 1/4 and then possibly make predictions.
I'm now seeing that even if I am using one regressor I'm still left with a multivariate time series and I've only found examples online for univariate models.
I'd appreciate if someone could give me ideas on how to model my problem using neural networks
I saw this link Keras RNN with LSTM cells for predicting multiple output time series based on multiple intput time series
but I just see a bunch of functions and nowhere where I can actually insert my function
The solution to your problem is the same as for the univariate case you found online except for the fact that you just need to work differently with your feature/independent set. your y variable or cost variable remains as is but your X variables will have to be in 3 dimensions which is (Number of observations, time steps, number of independent variables)
I was reading through all (or most) previously asked questions, but couldn't find an answer to my problem...
I have 13 variables measured on an ordinal scale (thy represent knowledge transfer channels), which I want to cluster (HCA) for a following binary logistic regression analysis (including all 13 variables is not possible due to sample size of N=208). A Factor Analysis seems inappropriate due to the scale level. I am using SPSS (but tried R as well).
Questions:
1: Am I right in using the Chi-Squared measure for count data instead of the (squared) euclidian distance?
2. How can I justify a choice of method? I tried single, complete, Ward and average, but all give different results and I can't find a source to base my decision on.
Thanks a lot in advance!
Answer 1: Since the variables are on ordinal scale, the chi-square test is an appropriate measurement test. Because, "A Chi-square test is designed to analyze categorical data. That means that the data has been counted and divided into categories. It will not work with parametric or continuous data (such as height in inches)." Reference.
Again, ordinal scaled data is essentially count or frequency data you can use regular parametric statistics: mean, standard deviation, etc Or non-parametric tests like ANOVA or Mann-Whitney U test to compare 2 groups or Kruskal–Wallis H test to compare three or more groups.
Answer 2: In a clustering problem, the choice of distance method solely depends upon the type of variables. I recommend you to read these detailed posts 1, 2,3
I am trying to extract common patterns that always appear whenever a certain event occurs.
For example, patient A, B, and C all had a heart attack. Using the readings from there pulse, I want to find the common patterns before the heart attack stroke.
In the next stage I want to do this using multiple dimensions. For example, using the readings from the patients pulse, temperature, and blood pressure, what are the common patterns that occurred in the three dimensions taking into consideration the time and order between each dimension.
What is the best way to solve this problem using Neural Networks and which type of network is best?
(Just need some pointing in the right direction)
and thank you all for reading
Described problem looks like a time series prediction problem. That means a basic prediction problem for a continuous or discrete phenomena generated by some existing process. As a raw data for this problem we will have a sequence of samples x(t), x(t+1), x(t+2), ..., where x() means an output of considered process and t means some arbitrary timepoint.
For artificial neural networks solution we will consider a time series prediction, where we will organize our raw data to a new sequences. As you should know, we consider X as a matrix of input vectors that will be used in ANN learning. For time series prediction we will construct a new collection on following schema.
In the most basic form your input vector x will be a sequence of samples (x(t-k), x(t-k+1), ..., x(t-1), x(t)) taken at some arbitrary timepoint t, appended to it predecessor samples from timepoints t-k, t-k+1, ..., t-1. You should generate every example for every possible timepoint t like this.
But the key is to preprocess data so that we get the best prediction results.
Assuming your data (phenomena) is continuous, you should consider to apply some sampling technique. You could start with an experiment for some naive sampling period Δt, but there are stronger methods. See for example Nyquist–Shannon Sampling Theorem, where the key idea is to allow to recover continuous x(t) from discrete x(Δt) samples. This is reasonable when we consider that we probably expect our ANNs to do this.
Assuming your data is discrete... you still should need to try sampling, as this will speed up your computations and might possibly provide better generalization. But the key advice is: do experiments! as the best architecture depends on data and also will require to preprocess them correctly.
The next thing is network output layer. From your question, it appears that this will be a binary class prediction. But maybe a wider prediction vector is worth considering? How about to predict the future of considered samples, that is x(t+1), x(t+2) and experiment with different horizons (length of the future)?
Further reading:
Somebody mentioned Python here. Here is some good tutorial on timeseries prediction with Keras: Victor Schmidt, Keras recurrent tutorial, Deep Learning Tutorials
This paper is good if you need some real example: Fessant, Francoise, Samy Bengio, and Daniel Collobert. "On the prediction of solar activity using different neural network models." Annales Geophysicae. Vol. 14. No. 1. 1996.
How to perform K-medioids clustering with Dynamic Time Warping as a distance measure in RapidMiner?
The idea with Dynamic Time Warping is to perform it on time series of different length. How can I do that in RapidMiner? I get this error message
The data contains missing values which is not allowed for KMediods
How can I cluster time series of different length?
You could fill the missing values with zeroes. The operator Replace Missing Values does this. I don't know the details of your data nor how RapidMiner calculates DTW distances so I therefore can't tell if this approach would yield valid results.
Faced with this, I might use the R extension with the dtw and cluster packages to investigate how distances between different length time series could be used to make clusters. Once you have R working, you can call it from RapidMiner.
I'm busy working on a project involving k-nearest neighbor (KNN) classification. I have mixed numerical and categorical fields. The categorical values are ordinal (e.g. bank name, account type). Numerical types are, for e.g. salary and age. There are also some binary types (e.g., male, female).
How do I go about incorporating categorical values into the KNN analysis?
As far as I'm aware, one cannot simply map each categorical field to number keys (e.g. bank 1 = 1; bank 2 = 2, etc.), so I need a better approach for using the categorical fields. I have heard that one can use binary numbers. Is this a feasible method?
You need to find a distance function that works for your data. The use of binary indicator variables solves this problem implicitly. This has the benefit of allowing you to continue your probably matrix based implementation with this kind of data, but a much simpler way - and appropriate for most distance based methods - is to just use a modified distance function.
There is an infinite number of such combinations. You need to experiment which works best for you. Essentially, you might want to use some classic metric on the numeric values (usually with normalization applied; but it may make sense to also move this normalization into the distance function), plus a distance on the other attributes, scaled appropriately.
In most real application domains of distance based algorithms, this is the most difficult part, optimizing your domain specific distance function. You can see this as part of preprocessing: defining similarity.
There is much more than just Euclidean distance. There are various set theoretic measures which may be much more appropriate in your case. For example, Tanimoto coefficient, Jaccard similarity, Dice's coefficient and so on. Cosine might be an option, too.
There are whole conferences dedicated to the topics of similarity search - nobody claimed this is trivial in anything but Euclidean vector spaces (and actually, not even there): http://www.sisap.org/2012
The most straight forward way to convert categorical data into numeric is by using indicator vectors. See the reference I posted at my previous comment.
Can we use Locality Sensitive Hashing (LSH) + edit distance and assume that every bin represents a different category? I understand that categorical data does not show any order and the bins in LSH are arranged according to a hash function. Finding the hash function that gives a meaningful number of bins sounds to me like learning a metric space.