Expectation Maximization (often abbreviated EM) is an iterative algorithm that can be used for maximum likelihood estimation in the presence of missing data or hidden variables.
Questions tagged [expectation-maximization]
125 questions
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EM Clustering with weka with log likelihood of 0 for some clusters? Confusing output
I have clustered 43574 time series using EM clusterer. The output is 24 clusters. I have some questions here. First, is it practically useful to deal with 24 clusters? Isn't it too much? If I am passing the results to neurosurgeon labelling these…
Parisan
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How to read const &vector data type from EM::getCov(&vector) for c++
I am trying to get the result of this function:
C++:
void EM::getCovs(std::vector& covs) const
My question is how to get the covs? I kept getting compiling error. Here is my code.
const vector &covs;
model->getCovs(covs);
I get error…
user3006123
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Using variable length data inputs with EM algorithm clustering
We have a set of sequences with taxi positions. We want to cluster the data by considering the sequential patterns in the data lines.
For example:
T1, T2, T3, T4 be the travels and a,b,c,d,e be set of places.
The data we have is like,
T1 b c b a…
MarterJay
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EM soft clustering in lingpipe
In Lingpipe's EM tutorial they said that it is possible to run the algorithm with no supervised data:
It is possible to train a classifier in a completely unsupervised fashion by having the initial classifier assign categories at random. Only the…
Tuan Anh Hoang-Vu
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Error in function Mclust() in package mclust
First, let's take a look of mydata:
head(mydata,10)
LONGITUDE LATITUDE
1 121.7779 39.0476
2 121.5210 38.8771
3 121.6259 38.9224
4 121.5907 38.8980
5 121.5865 38.8816
6 121.5808 38.9121
7 121.5806 38.8843
8 121.5907 …
Ling Zhang
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