One of the practice quiz question(not homework) is asking to find how many core points in one dimensional points with given EPS and MinPTS. I thought DBSCAN should be used for only two dimensions. Any guidance is much appreciated.
Question
Yes, DBSCAN can be used on 1 dimensional data.
It's not particularly smart (just use Kernel Density Estimation instead) but why do you think this would not work?
Related
I've been currently working on my FYP on Brain tumor classification.Extracted features using wavelet transform ,glcm ,polynomial transform etc.
IS IT RIGHT TO APPEND THESE FEATURE VECTORS (columnwise) for training? like combinations of these feature vectors eg: glcm+wavelet
Can you suggest me any papers related to this?
THANK YOU FOR THE HELP
Yes, this method is known as early fusion.
In other words, early fusion is when you are concatenating 2 or more features sets prior to model training.
There are a number of other methods for feature fusion, including model-, and late-fusion.
Take a look at these papers which might help you:
Specific to a health-based application
figure which might help you to grasp the concept
I have a dataset that overlaps a lot. So far my results with SVM are not good. Do you have any recomendations for a model that may be able to differ between these 2 datasets?
Scatter plot from both classes
It is easy to fit the dataset by interpolation of one of the classes and predicting the other one otherwise. The problem with this approach is though, that it will not generalize well. The question you have to ask yourself is, if you can predict the class of a point given its attributes. If not then every ML algorithm will also fail to do so.
Then the only reasonable thing you can do is to collect more data and more attributes for every point. Maybe by adding a third dimension you can seperate the data more easily.
If the data is overlapping so much, both should be of the same class, but we know they are not. So, there is/are some feature(s) or variable(s) that is/are separating these data points into two classes. Try to add more features for data.
And sometimes, just transforming the data into a different scale can help.
Both the classes need not be equally distributed, as skewed data distribution can be handled separately.
First of all, what is your criterion for "good results"? What style of SVM did you use? Simple linear will certainly fail for most concepts of "good", but a seriously convoluted Gaussian kernel might dredge something out of the handfuls of contiguous points in the upper regions of the plot.
I suggest that you run some basic statistics on the data you've presented, to see whether they're actually as separable as you'd want. I suggest a T-test for starters.
If you have other dimensions, I strongly recommend that you use them. Start with the greatest amount of input you can handle, and reduce from there (principal component analysis). Until we know the full shape and distribution of the data, there's not much hope of identifying a useful algorithm.
That said, I'll make a pre-emptive suggestion that you look into spectral clustering algorithms when you add the other dimensions. Some are good with density, some with connectivity, while others key on gaps.
I have visualized a dataset in 2D after employing PCA. 1 dimension is time and the Y dimension is First PCA component. As figure shows, there is relatively good separation between points (A, B). But unfortunately clustering methods (DBSCAN, SMO, KMEANS, Hierarchical) are not able to cluster these points in 2 clusters. As you see in section A there is a relative continuity and this continuous process is finished and Section B starts and there is rather big gap in comparison to past data between A and B.
I will be so grateful if you can introduce me any method and algorithm (or devising any metric from data considering its distribution) to be able to do separation between A and B without visualization. Thank you so much.
This is plot of 2 PCA components for the above plot(the first one). The other one is also the plot of components of other dataset which I get bad result,too.
This is a time series, and apparently you are looking for change points or want to segment this time series.
Do not treat this data set as a two dimensional x-y data set, and don't use clustering here; rather choose an algorithm that is actually designed for time series.
As a starter, plot series[x] - series[x-1], i.e. the first derivative. You may need to remove seasonality to improve results. No clustering algorithm will do this, they do not have a notion of seasonality or time.
If PCA gives you a good separation, you can just try to cluster after projecting your data through your PCA eigenvectors. If you don't want to use PCA, then you will need anyway an alternative data projection method, because failing clustering methods imply that your data is not separable in the original dimensions. You can take a look at non linear clustering methods such as the kernel based ones or spectral clustering for example. Or to define your own non-euclidian metric, which is in fact just another data projection method.
But using PCA clearly seems to be the best fit in your case (Occam razor : use the simplest model that fits your data).
I don't know that you'll have an easy time devising an algorithm to handle this case, which is dangerously (by present capabilities) close to "read my mind" clustering. You have a significant alley where you've marked the division. You have one nearly as good around (1700, +1/3), and an isolate near (1850, 0.45). These will make it hard to convince a general-use algorithm to make exactly one division at the spot you want, although that one is (I think) still the most computationally obvious.
Spectral clustering works well at finding gaps; I'd try that first. You might have to ask it for 3 or 4 clusters to separate the one you want in general. You could also try playing with SVM (good at finding alleys in data), but doing that in an unsupervised context is the tricky part.
No, KMeans is not going to work; it isn't sensitive to density or connectivity.
I am working with some points which are very compact together and therefore forming clusters amongst them is proving very difficult. Since I am new to this concept, I read in a paper about the concept of Gaussian weighting the points randomly or rather resampling using gaussian weight.
My question here is how are gaussian weight applied to the data points? Is it the actual normal distribution where I have to compute the means and the variance and SD and than randomly sample or there is other ways to do it. I am confused on this concept?
Can I get some hints on the concept please
I think you should look at book:
http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738
There is a good chapters on modeling point distributions.
I'm pretty new in the field of machine learning (even if I find it extremely interesting), and I wanted to start a small project where I'd be able to apply some stuff.
Let's say I have a dataset of persons, where each person has N different attributes (only discrete values, each attribute can be pretty much anything).
I want to find clusters of people who exhibit the same behavior, i.e. who have a similar pattern in their attributes ("look-alikes").
How would you go about this? Any thoughts to get me started?
I was thinking about using PCA since we can have an arbitrary number of dimensions, that could be useful to reduce it. K-Means? I'm not sure in this case. Any ideas on what would be most adapted to this situation?
I do know how to code all those algorithms, but I'm truly missing some real world experience to know what to apply in which case.
K-means using the n-dimensional attribute vectors is a reasonable way to get started. You may want to play with your distance metric to see how it affects the results.
The first step to pretty much any clustering algorithm is to find a suitable distance function. Many algorithms such as DBSCAN can be parameterized with this distance function then (at least in a decent implementation. Some of course only support Euclidean distance ...).
So start with considering how to measure object similarity!
In my opinion you should also try expectation-maximization algorithm (also called EM). On the other hand, you must be careful while using PCA because this algorithm may reduce the dimensions relevant to clustering.