How to avoid large size cluster in kmeans clustering? - machine-learning

We have list of 10000 sentences and we want to cluster the similar sentences using kmeans clustering algorithm. Once we train our model, we get around 120 clusters. But one of the cluster got around 6000 sentences in it and these sentences are quite un-similar. Not sure on why this issue occurs, could anyone please help ?

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Hierarchical Clustering

I have read some resources and I found out how hierarchical clustering works. However, when I compare it with k-means clustering, it seems to me that k-means really constitues specific number of clusters,whereas hierarchical analysis shows me how the samples can be clustered. What I mean is that I do not get a specific number of clusters in hierarchical clustering. I get only a scheme about how the clusters can be constituted and portion of relation between the samples.
Thus, I cannot understand where I can use this clustering method.
Hierarchical clustering (HC) is just another distance-based clustering method like k-means. The number of clusters can be roughly determined by cutting the dendrogram represented by HC. Determining the number of clusters in a data set is not an easy task for all clustering methods, which is usually based on your applications. Tuning the thresholds in HC may be more explicit and straightforward for researchers, especially for a very large data set. I think this question is also related.
In k-means clustering k is a hyperparameter that you need to find in order to divide your data points into clusters whereas in hierarchical clustering (lets take one type of hierarchical clustering i.e. agglomerative) firstly you consider all the points in your dataset as a cluster and then merge two clusters based on a similarity metric and repeat this until you get a single cluster. I will explain this with an example.
Suppose initially you have 13 points (x_1,x_2,....,x_13) in your dataset so at start you will have 13 clusters, now in second step lets you get 7 clusters (x_1-x_2 , x_4-x_5, x_6-x_8, x_3-x_7, x_11-x_12, x_10, x_13) based on the similarity between the points. In the third step lets say you get 4 clusters(x_1-x_2-x_4-x_5, x_6-x_8-x_10, x_3-x_7-x_13, x_11-x_12) like this you would arrive to a step wherein all the points in your dataset form one cluster and which is also the last step of agglomerative clustering algorithm.
So in hierarchical clustering, there is no hyperparameter, depending upon your problem, if you want 7 clusters then stop at the second step if you want 4 clusters then stop at the third step and likewise.
A practical advantage in hierarchical clustering is the possibility of visualizing results using dendrogram. If you don’t know in advance what number of clusters you’re looking for (as is often the case…), you can use the dendrogram plot that can help you choose k with no need to create separate clusterings. Dendrogram can also give a great insight into the data structure, help identify outliers, etc. Hierarchical clustering is also deterministic, whereas k-means with random initialization can give you different results when running several times on the same data.
Hope this helps.

dimensional time series data clustering

I have a data set which is time-series type and contains three dimensions namely acceleration, speed and grade. I want to apply clustering to identify the clusters that have similar speed (acceleration=0, positive or negative) varying with grade. I do not know what type of clustering should i use, surely k-means cannot help me because there is a serial correlation between my data point because each point is affected by its previous point. Could you please help me with the type of clustering?
Popular time series similarity metrics such as DTW can be implemented for multiple variates the same way as for a single variate. The most challenging part is normalization.
You then can run hierarchical clustering trivially. Do not use KMeans.

Supervised Machine Learning algorithms

I am trying to classify a data using Supervised machine learning algorithms.Everything's working fine, but just for my curiosity, I tried 6 classification algorithms simultaneously on a single data set. Steps followed are as follows-
1> Train all the algorithms.
2> predicted the result(either 1 or 0) for all test_data individually, by all algorithms.
3> If most of the algos gave 0, i considered the result for that data pair to be 0, similarly for result 1.
4> Then i found out the overall accuracy.
I expected the overall accuracy to be higher then the individual results(By each algorithm working individually), But i got almost the average accuracy.(Average here means average of accuracies of individual algos).
Can anyone please help me to find the reason?
This depends on the algorithms you picked. Many algorithms are sensitive to different things. For instance, k-means, linear SVM, and power iteration clustering will get markedly different results.
You got what you asked for: you averaged the votes, without coordinating the algorithms in any way. You got an average result.
I doubt that weighted averaging will help much; all you're doing there is training a meta-model. Instead, consider the data set you have. You need to research modelling algorithms and pick one that tends to work well on the statistical shape of your data set with respect to the desired purpose. Since you've given us none of this background, we can't help with specifics.

How To Fight Randomness Caused By KMeans Clustering

I'm developing an algorithm to classify different types of dogs based off of image data. The steps of the algorithm are:
Go through all training images, detect image features (ie SURF), and extract descriptors. Collect all descriptors for all images.
Cluster within the collected image descriptors and find k "words" or centroids within the collection.
Reiterate through all images, extract SURF descriptors, and match the extracted descriptor with the closest "word" found via clustering.
Represent each image as a histogram of the words found in clustering.
Feed these image representations (feature vectors) to a classifier and train...
Now, I have run into a bit of a problem. Finding the "words" within the collection of image descriptors is a very important step. Due to the random nature of clustering, different clusters are found each time I run my program. The unfortunate result is that sometimes the accuracy of my classifier will be very good, and other times, very bad. I have chalked this up to the clustering algorithm finding "good" words sometimes, and "bad" words other times.
Does anyone know how I can hedge against the clustering algorithm from finding "bad" words? Currently I just cluster several times and take the mean accuracy of my classifier, but there must be a better way.
Thanks for taking time to read through this, and thank you for your help!
EDIT:
I am not using KMeans for classification; I am using a Support Vector Machine for classification. I am using KMeans for finding image descriptor "words", and then using these words to create histograms which describe each image. These histograms serve as feature vectors that are fed to the Support Vector Machine for classification.
There are many possible ways of making clustering repeatable:
The most basic method of dealing with k-means randomness is simply running it multiple times and selecting the best one (the one that minimizes the inner cluster distances/maximizes the between clusters distance).
One can use some fixed initialization for your data instead of randomization. There are many heuristics for starting the k-means. Or at least minimize the variance by using algorithms like k-means++.
Use modification of k-means which guarantees global minimum of regularized function, ie. convex k-means
Use different clustering method, which is deterministic, ie. Data Nets
I would offer two possible suggestions, in addition to those provided.
K-means optimises an objective related to the distance between cluster points and their centroids. You care about classification accuracy. Depending on the computational cost, a simple brute-force approach is to induce multiple clusterings on a subset of your training data, and evaluate the performance of each on some held-out development set for the task you care about. Then use the highest performing variant as the final model. I don't like the use of non-random initialisation because this is only a solution to avoid the randomness, not find the true global minimum of the objective, and your chosen initialisation may be useless and just produce consistently bad classifiers.
The other approach, which is much harder, is to view the k-means step as a dimensionality reduction to enable classification, and incorporate this into the classifier directly. If you use a deep neural net, the layer(s) closest to the input are essentially dimensionality reducers in the same way as the k-means clustering you induce: the difference is their weights are set wrt the error of the net on the classification problem, rather than some unrelated intermediate step. The downside is that this is much closer to a current research problem: training deep nets is hard. You could start with a standard one-hidden-layer architecture (with binary activations on the hidden layer, and using cross-entropy loss on the output layer with outputs coded as one-of-n categories), and attempt to add layers incrementally, but as far as I'm aware standard training algorithms start to behave poorly beyond the single hidden layer, so you'd need to investigate layer-wise training to initialise, or some of the Hessian-Free stuff coming out of Geoff Hinton's group in Toronto.
That is actually an important problem with the BofW approach, and you should share this prominently. SIFT data may actually not have k-means clusters at all. However, due to the nature of the algorithm, k-means will always produce k clusters. One of the things to test with k-means is to validate that the results are stable. If you get a completely different result each time, they are not much better than random.
Nevertheless, if you just want to get some working results, you can just fix the dictionary once and choose one that is working well.
Or you might look into more advanced clustering (in particular one that is more robust wrt. noise!)

Bad clustering results with mahout on Reuters 21578 dataset

I 've used a part of reuters 21578 dataset and mahout k-means for clustering.To be more specific I extracted only the texts that has a unique value for category 'topics'.So I ve been left with 9494 texts that belong to one among 66 categories. I ve used seqdirectory to create sequence files from texts and then seq2sparse to crate the vectors. Then I run k-means with cosine distance measure (I ve tried tanimoto and euclidean too, with no better luck), cd=0.1 and k=66 (same as the number of categories). So I tried to evaluate the results with silhouette measure using custom Java code and the matlab implementation of silhouette (just to be sure that there is no error in my code) and I get that the average silhouette of the clustering is 0.0405. Knowing that the best clustering could give an average silhouette value close to 1, I see that the clustering result I get is no good at all.
So is this due to Mahout or the quality of catgorization on reuters dataset is low?
PS: I m using Mahout 0.7
PS2: Sorry for my bad English..
I've never actually worked with Mahout, so I cannot say what it does by default, but you might consider checking what sort of distance metric it uses by default. For example, if the metric is Euclidean distance on unnormalized document word counts, you can expect very poor quality cluster quality, as document length will dominate any meaningful comparison between documents. On the other hand, something like cosine distance on normalized, or tf-idf weighted word counts can do much better.
One other thing to look at is the distribution of topics in the Reuters 21578. It is very skewed towards a few topics such as "acq" or "earn", while others are used only handfuls of times. This can it difficult to achieve good external clustering metrics.

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