Which is the best method for document classification if time is not a factor, and we dont know how many classes there are?
In my (incomplete) knowledge, Hierarchical Agglomerative Clustering is the best approach if you don't know how many classes. All of the other clustering algorithms either require prior knowledge of the number of buckets or some sort of cross-validation or other experimentation to determine a sensible number of buckets.
A cross link: see how-do-i-determine-k-when-using-k-means-clustering on SO.
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I try to apply One Class SVM but my dataset contains too many features and I believe feature selection would improve my metrics. Are there any methods for feature selection that do not need the label of the class?
If yes and you are aware of an existing implementation please let me know
You'd probably get better answers asking this on Cross Validated instead of Stack Exchange, although since you ask for implementations I will answer your question.
Unsupervised methods exist that allow you to eliminate features without looking at the target variable. This is called unsupervised data (dimensionality) reduction. They work by looking for features that convey similar information and then either eliminate some of those features or reduce them to fewer features whilst retaining as much information as possible.
Some examples of data reduction techniques include PCA, redundancy analysis, variable clustering, and random projections, amongst others.
You don't mention which program you're working in but I am going to presume it's Python. sklearn has implementations for PCA and SparseRandomProjection. I know there is a module designed for variable clustering in Python but I have not used it and don't know how convenient it is. I don't know if there's an unsupervised implementation of redundancy analysis in Python but you could consider making your own. Depending on what you decide to do it might not be too tricky (especially if you just do correlation based).
In case you're working in R, finding versions of data reduction using PCA will be no problem. For variable clustering and redundancy analysis, great packages like Hmisc and ClustOfVar exist.
You can also read about other unsupervised data reduction techniques; you might find other methods more suitable.
my data contain several features on user level.
and my desire is to cluster them to several groups based on this features
my data is skewed with presence of extreme outliers for of some of the features.
my question is what is the best practice for pre-processing before the clustering algorithm ?
The best practice for clustering is to first figure out how to measure distance reliably. Then many clustering methods can be tried.
But before you can quantify dissimilarity, the data cannot be used for most clustering.
Well, I have been studying up on the different algorithms used for clustering like k-means, k-mediods etc and I was trying to run the algorithms and analyze their performance on the leaf dataset right here:
http://archive.ics.uci.edu/ml/datasets/Leaf
I was able to cluster the dataset via k-means by first reading the csv file, filtering out unneeded attributes and applying k-means on it. The problem that I am facing here that I wish to calculate measures such as entropy, precision, recall and f-measure for the model developed via k-means. Is there an operator avialable that allows me to do this so that I can quantitatively compare the different clustering algorithms available on rapid-miner?
P.S I know about performance operators like Performance(Classification) that allows me to calculate precision and recall for a model but I dont know any that allow me to calculate entropy.
Help would be much appreciated.
The short answer is to use R. Here's a link to a book chapter about this very subject. There is a revised version coming soon that works for the most recent version of RapidMiner.
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'm using WEKA for my thesis and have over 1000 lines of data. The database includes demographical information (Age, Location, status etc.) followed by name of products (valued 1 or 0). The end results is a recommender system.
I used two methods of clustering, K-Means and DBScan.
When using K-means I tried 3 different number of cluster, while using DBscan I chose 3 different epsilons (Epsilon 3 = 48 clusters with ignored 17% of data, Epsilone 2.5 = 19 clusters while cluster 0 holds 229 items with ignored 6%.) Meaning i have 6 different clustering results for same data.
How do I choose what's best suits my data ?
What is "best"?
As some smart people noticed:
the validity of a clustering is often in the eye of the beholder
There is no objectively "better" for clustering, or you are not doing cluster analysis.
Even when a result actually is "better" on some mathematical measure such as separation, silhouette or even when using a supervised evaluation using labels - its still only better at optimizing towards some mathematical goal, not to your use case.
K-means finds a local optimal sum-of-squares assignment for a given k. (And if you increase k, there exists a better assignment!) DBSCAN (it's actually correctly spelled all uppercase) always finds the optimal density-connected components for the given MinPts/Epsilon combination. Yet, both just optimize with respect to some mathematical criterion. Unless this critertion aligns with your requirements, it is worthless. So there is no best, until you know what you need. But if you know what you need, you would not need to do cluster analysis.
So what to do?
Try different algorithms and different parameters and analyze the output with your domain knowledge, if they help you with the problem you are trying to solve. If they help you solving your problem, then they are good. If they do not help, try again.
Over time, you will collect some experience. For example, if the sum-of-squares is meaningless for your domain, don't use k-means. If your data does not have meaningful density, don't use density based clustering such as DBSCAN. It's not that these algorithms fail. They just don't solve your problem, they solve a different problem that you are not interested in. And they might be really good at solving this other problem...