I am working on instances from the TSPLIB, which are simply coordinates of nodes in a plan. I'm looking to analyze spatial characteristics and features of a set of instances (e.g. clustered, not clustered, dispersed, etc) and I would like to implement some code in Matlab to analyze and compute specific features.
For example, so far, I have used Nearest Neighbor analysis to identify clusters, as well as quadrant analysis. Can anyone suggest any other spatial features and patterns that could be computed with some relatively simple code? Anybody maybe expert in the Traveling Salesman Problem. Thank you so much!
K-means is a very useful clustering tool that you can use.
https://www.mathworks.com/help/stats/kmeans.html
Nearest Neighbor is a classification methods. if you want to do classification you can use K Nearest Neighbors, SVM or Neural Networks Pattern recognition toolbox. these are all already in Matlab.
Also, check out Matlab Apps. there are some very cool clustering tools available as well with examples.
Related
I am new to machine learning and now I am interested in document clustering (short texts with different lengths) according to their semantic similarity (I just want to go beyond the standard TF/IDF approach). I read the paper http://proceedings.mlr.press/v37/kusnerb15.pdf where the Word Mover's distance for word embeddings is explained. In the paper they used it for classification. My question is now - can I use it for clustering? If so, is there a paper where this kind of usage is discribed?
P.S.: I am basically interested in clustering which takes into account the semantic similarity, so even a word2vec or doc2vec approach will do the job - I just couldn't find any papers where they are used in a clustering problem.
If you could afford to compute an entire distance matrix, then you could do hierarchical clustering, for example.
It's easy today find other clusterings that accept any distance and use a threshold. These could even use the bounds for performance. But it's not obvious that they will work on such data.
I have a data set which consists of data points having attributes like:
average daily consumption of energy
average daily generation of energy
type of energy source
average daily energy fed in to grid
daily energy tariff
I am new to clustering techniques.
So my question is which clustering algorithm will be best for such kind of data to form clusters ?
I think hierarchical clustering is a good choice. Have a look here Clustering Algorithms
The more simple way to do clustering is by kmeans algorithm. If all of your attributes are numerical, then this is the easiest way of doing the clustering. Even if they are not, you would have to find a distance measure for caterogical or nominal attributes, but still kmeans is a good choice. Kmeans is a partitional clustering algorithm... i wouldn't use hierarchical clustering for this case. But that also depends on what you want to do. you need to evaluate if you want to find clusters within clusters or they all have to be totally apart from each other and not included on each other.
Take care.
1) First, try with k-means. If that fulfills your demand that's it. Play with different number of clusters (controlled by parameter k). There are a number of implementations of k-means and you can implement your own version if you have good programming skills.
K-means generally works well if data looks like a circular/spherical shape. This means that there is some Gaussianity in the data (data comes from a Gaussian distribution).
2) if k-means doesn't fulfill your expectations, it is time to read and think more. Then I suggest reading a good survey paper. the most common techniques are implemented in several programming languages and data mining frameworks, many of them are free to download and use.
3) if applying state-of-the-art clustering techniques is not enough, it is time to design a new technique. Then you can think by yourself or associate with a machine learning expert.
Since most of your data is continuous, and it reasonable to assume that energy consumption and generation are normally distributed, I would use statistical methods for clustering.
Such as:
Gaussian Mixture Models
Bayesian Hierarchical Clustering
The advantage of these methods over metric-based clustering algorithms (e.g. k-means) is that we can take advantage of the fact that we are dealing with averages, and we can make assumptions on the distributions from which those average were calculated.
I just detected faces using Viola-jones algorithm. I cropped faces from frames(or video)and I made it as training set.In my video there are 5 different faces. I decided to use eigenfaces for face recognition.I ended with finding eucledian distance for a input image.What Iam supposed do now.Whether I have to use classification or clustering technique?In my project they told to use kmeans clustering.How it can be done using eucledian distance explain?Give some useful links so that I can understand in better way.
When you have 5 different faces, this sounds like classification to me. That is a label column!
I don't think k-means will get you anywhere. When you have high-dimensional data (such as images), k-means performs as good as random convex partitioning. I.e. it's largely useless.
I am trying to differentiate two populations. Each population is an NxM matrix in which N is fixed between the two and M is variable in length (N=column specific attributes of each run, M=run number). I have looked at PCA and K-means for differentiating the two, but I was curious of the best practice.
To my knowledge, in K-means, there is no initial 'calibration' in which the clusters are chosen such that known bimodal populations can be differentiated. It simply minimizes the distance and assigns the data to an arbitrary number of populations. I would like to tell the clustering algorithm that I want the best fit in which the two populations are separated. I can then use the fit I get from the initial clustering on future datasets. Any help, example code, or reading material would be appreciated.
-R
K-means and PCA are typically used in unsupervised learning problems, i.e. problems where you have a single batch of data and want to find some easier way to describe it. In principle, you could run K-means (with K=2) on your data, and then evaluate the degree to which your two classes of data match up with the data clusters found by this algorithm (note: you may want multiple starts).
It sounds to like you have a supervised learning problem: you have a training data set which has already been partitioned into two classes. In this case k-nearest neighbors (as mentioned by #amas) is probably the approach most like k-means; however Support Vector Machines can also be an attractive approach.
I frequently refer to The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) by Trevor Hastie (Author), Robert Tibshirani (Author), Jerome Friedman (Author).
It really depends on the data. But just to let you know K-means does get stuck at local minima so if you wanna use it try running it from different random starting points. PCA's might also be useful how ever like any other spectral clustering method you have much less control over the clustering procedure. I recommend that you cluster the data using k-means with multiple random starting points and c how it works then you can predict and learn for each the new samples with K-NN (I don't know if it is useful for your case).
Check Lazy learners and K-NN for prediction.
In my clustering problem, not only the points can come and go but also the features can be removed or added. Is there any clustering algorithm for my problem.
Specifically I am looking for an agglomerative hierarchical clustering version of these kind of clustering algorithms.
You can use hierarchical clustering (except it scales really bad) or any other distance based clustering. Just k-means is a bit tricky because how do you compute the mean when the value is not present?
You only need to define an appropriate distance function first.
Clustering is usually done based on similarity, so: first find out what "similar" means for you. This is very data set and use case specific, although many people can use some kind of distance function. There is no "one size fits all" solution.