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.
Related
I am trying to build my first recommender system where i create a user feature space and then cluster them into different groups. Then for the recommendation to work for a particular user , first i find out the cluster to which the user belongs and then recommend entities(items) in which his/her nearest neighbor showed interest. The data which i am working on is high dimensional and sparse. Before implementing the above approach, there are few questions, whose answers might help me in adopting a better approach.
As my data is high dimensional and sparse, should i go for dimensionality reduction and then apply clustering or should I go for an algorithm like spherical K-means which works on sparse high dimensional data?
How should I find the nearest neighbors after creating clusters of users.(Which distance measure should i take as i have read that Euclidean distance is not a good measure for high dimensional data)?
It's not obvious that clustering is the right algorithm here. Clustering is great for data exploration and analysis, but not always for prediction. If your end product is based around the concept of "groups of like users" and the items they share, then go ahead with clustering and simply present a ranked list of items that each user's cluster has consumed (or a weighted average rating, if you have preference information).
You might try standard recommender algorithms that work in sparse high-dimensional situations, such as item-item collaborative filtering or sparse SVD.
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.
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.
Is there any algorithm or trick of how to determine the number of gaussians which should be identified within a set of data before applying the expectation maximization algorithm?
For example, in the above illustrated plot of 2 - Dimensional data, when I apply the Expectation Maximization algorithm, I try to fit 4 gaussians to the data and I would obtain the following result.
But what if I wouldn't knew the number of gaussians within the data? Is there any algorithm or trick which I could apply so that I could find out this detail?
This might be a bit of a retread, since others already linked the wiki article of the actual cluster number determination, but I found that article a lil overly dense, so I thought I'd provide a brief, intuitive answer:
Basically, there isn't a universally 'correct' answer for the number of clusters in a data set -- the fewer clusters, the smaller the description length but the higher the variance, and in all non-trivial datasets the variance won't completely go away unless you have a Gaussian for each point, which renders the clustering useless (this is a case of the more general phenomena known as the 'futility of bias free learning': A learner that makes no a priori assumptions regarding the identity of the target concept has no rational basis for classifying any unseen instances).
So you basically have to pick some feature of your dataset to maximize via the number of clusters (see the wiki article on inductive bias for some example features)
In other sad news, in all such cases finding the number of clusters is known to be NP-hard, so the best you can expect is a good heuristic approach.
Wikipedia has an article on this subject. I am not too familiar with the subject, but I've been told that clustering algorithms that don't require specifying the number of clusters instead need some density information about the clusters or some minimum distance between clusters.
Non parametric bayesian clustering is now getting lot of attention. You dont need to specify clusters.
Autoclass is algorithm that automatically identify number of clusters from mixture.
What is the most popular text clustering algorithm which deals with large dimensions and huge dataset and is fast?
I am getting confused after reading so many papers and so many approaches..now just want to know which one is used most, to have a good starting point for writing a clustering application for documents.
To deal with the curse of dimensionality you can try to determine the blind sources (ie topics) that generated your dataset. You could use Principal Component Analysis or Factor Analysis to reduce the dimensionality of your feature set and to compute useful indexes.
PCA is what is used in Latent Semantic Indexing, since SVD can be demonstrated to be PCA : )
Remember that you can lose interpretation when you obtain the principal components of your dataset or its factors, so you maybe wanna go the Non-Negative Matrix Factorization route. (And here is the punch! K-Means is a particular NNMF!) In NNMF the dataset can be explained just by its additive, non-negative components.
There is no one size fits all approach. Hierarchical clustering is an option always. If you want to have distinct groups formed out of the data, you can go with K-means clustering (it is also supposedly computationally less intensive).
The two most popular document clustering approaches, are hierarchical clustering and k-means. k-means is faster as it is linear in the number of documents, as opposed to hierarchical, which is quadratic, but is generally believed to give better results. Each document in the dataset is usually represented as an n-dimensional vector (n is the number of words), with the magnitude of the dimension corresponding to each word equal to its term frequency-inverse document frequency score. The tf-idf score reduces the importance of high-frequency words in similarity calculation. The cosine similarity is often used as a similarity measure.
A paper comparing experimental results between hierarchical and bisecting k-means, a cousin algorithm to k-means, can be found here.
The simplest approaches to dimensionality reduction in document clustering are: a) throw out all rare and highly frequent words (say occuring in less than 1% and more than 60% of documents: this is somewhat arbitrary, you need to try different ranges for each dataset to see impact on results), b) stopping: throw out all words in a stop list of common english words: lists can be found online, and c) stemming, or removing suffixes to leave only word roots. The most common stemmer is a stemmer designed by Martin Porter. Implementations in many languages can be found here. Usually, this will reduce the number of unique words in a dataset to a few hundred or low thousands, and further dimensionality reduction may not be required. Otherwise, techniques like PCA could be used.
I will stick with kmedoids, since you can compute the distance from any point to anypoint at the beggining of the algorithm, You only need to do this one time, and it saves you time, specially if there are many dimensions. This algorithm works by choosing as a center of a cluster the point that is nearer to it, not a centroid calculated in base of the averages of the points belonging to that cluster. Therefore you have all possible distance calculations already done for you in this algorithm.
In the case where you aren't looking for semantic text clustering (I can't tell if this is a requirement or not from your original question), try using Levenshtein distance and building a similarity matrix with it. From this, you can use k-medoids to cluster and subsequently validate your clustering through use of silhouette coefficients. Unfortunately, Levensthein can be quite slow, but there are ways to speed it up through uses of thresholds and other methods.
Another way to deal with the curse of dimensionality would be to find 'contrasting sets,', conjunctions of attribute-value pairs that are more prominent in one group than in the rest. You can then use those contrasting sets as dimensions either in lieu of the original attributes or with a restricted number of attributes.