0
votes
0answers
11 views

Grouping search queries by similarity of search results

we run some studies using google search queries. We need to cluster these queries in topics and we would like to find some unsupervised approach. for each query we have the search results. I ...
0
votes
0answers
10 views

Dirichlet process mixture model in Python

My question is concerned with the practical issues of using this model. I've tried to use Dirichlet process mixture model from Scikit learn python package to find a number of clusters in my data (1D ...
1
vote
1answer
47 views

How to evaluate a clustering/unsupervised learning problem with massive amounts of data, with labels only for a small fraction of points

I'm wondering if anybody can point me to work on the evaluation of unsupervised learning where there are a very large (say hundreds of millions) number of points and manual labelling can only ever be ...
0
votes
0answers
23 views

How to measure the similarity of k-means clustering using different datasets?

I run k-means clustering on my dataset (100 samples in total) and partition the data into k=5 clusters. Then I want to test how robust of the k-means can be; however, I haven't got more new data ...
0
votes
2answers
29 views

Suggestion for discovering inherent patterns in data

I have a a big data set of clients with all sorts of variables that describe their background, payment history, and more... I also have a subset of those client who all have portrayed similar ...
0
votes
0answers
41 views

Justifying unsupervised clustering using Random Forest?

I have been looking at ways to carry out unsupervised clustering of data with both numeric and nominal (but not ordinal) variables. I also suspect non-linearity in the data. A possible solution would ...
1
vote
1answer
35 views

A clustering and classification question

I'm trying to classify my set of data into two classes (introvert / extrovert). I was thinking of using a decision tree at first, but I don't have any potential known results in order to create my ...
1
vote
0answers
62 views

Unsupervised Random Forest for Visual Codebook generation

I'm trying to apply the bag of visual words approach to make scene classification. I started to use k-means to generate my codebook, but rapidly discovered its limitations. From one codebook ...
1
vote
0answers
16 views

Heuristics for unsupervised or semi-supervised approaches to GIS coordinate data

I have a more conceptual/heuristic question about how to go about formulating a problem in order to take a semi- or unsupervised method of solving it. I'm working on a project with data collected ...
0
votes
0answers
22 views

Number of variables when using Self-Organizing Map

I have a dataset containing $p$ variables (or columns) denoted by $X_i$ for $i=1,...,p$. I am trying to cluster this dataset using Self-Organizing Map. There are 3 main variables within these $p$ ...
1
vote
1answer
44 views

The representation of a high-dimensional data set by a low number of data points

I know that some of the questions I am asking here have been answered in a general case in the two questions I am referring to in the problem section. Nonetheless, I am asking for a very specific case ...
4
votes
3answers
243 views

Can you compare different clustering methods on a dataset with no ground truth by cross-validation?

Currently, I am trying to analyze a text document dataset that has no ground truth. I was told that you can use k-fold cross validation to compare different clustering methods. However, the examples I ...
0
votes
1answer
100 views

Principles of Time Series Clustering

I would like to understand complexity of time series clustering. Clustering is similarity based, so as a basic step we evaluate distance between to points in a multidimensional space. In time series, ...
6
votes
2answers
239 views

Why only the mean value is used in (K-means) clustering method?

In clustering methods such as K-means, the euclidean distance is the metric to use. As a result, we only calculate the mean values within each cluster. And then adjustments are made on the elements ...
4
votes
1answer
502 views

Performance metrics to evaluate unsupervised learning

With respect to the unsupervised learning (like clustering), are there any metrics to evaluate performance?
0
votes
1answer
59 views

Is it necessary to split data in clustering like in supervised learning?

I'm learning clustering analysis and one book I read says the clustering model should be applied to a disjoint data set to examine the consistency of the model. I think in clustering analysis we ...
4
votes
1answer
183 views

Self organizing maps vs. kernel k-means

For an application, I want to cluster data (potentially high dimensional) and extract probability of belonging to a cluster. I consider at the moment Self organizing maps or kernel k-means to do the ...
1
vote
0answers
33 views

Anomaly/outlier detection using fuzzy clustering

I understand that fuzzy clustering using FCM produces a membership matrix for the set of data points we feed to it. What characteristics will an anomalous cluster produced during this method have? ...
1
vote
1answer
104 views

Using the self-organizing map for sequences of categorical data

I have a number of vectors of categorical data (ex. {'re','ty','cf', ...} ) and I want to perform an unsupervised learning on them. I came across ...
2
votes
0answers
42 views

Univariate clustering for longitudinal cohort

We have screening information on thousands of patients followed for several years. We also have their cancer outcomes, whether or not such cancers were identified by screening or were otherwise ...
2
votes
1answer
120 views

Is it necessary for a distance measure used in clustering to correspond to some valid vector space?

I have defined an distance measure based on some properties of points. But I'm not even sure that it corresponds to a valid distance in some vector space. Is this a necessary condition for clustering ...
2
votes
2answers
145 views

Choosing which data-point to label (active learning)

For an online unsupervised learning algorithm, data-points are learned sequentially. The performance may improve if in addition to the unlabelled data we have some labelled data-points (i.e. ...
0
votes
2answers
55 views

Compute probability of a grouping being correct

I have an exemplar grouping of objects (each with their own feature vector) into categories. I am then given a new grouping of compeltely different objects, and Iw would like to compute the ...
1
vote
2answers
519 views

Image Clustering with K-means - Postprocessing

I did some clustering on an image (each pixel is an observation that has 5 variables associated with it), I get pretty detailed results but they are a little bit noisey... I think. I used K-means. ...
12
votes
4answers
326 views

How to measure shape of cluster?

I know that this question is not well defined, but some clusters tend to be elliptical or lie in lower dimensional space whilst the other have nonlinear shapes (in 2D or 3D examples). Is there any ...
2
votes
1answer
346 views

Which are the most effective clustering ensembles?

In supervised learning, there are some ensemble methods that overcome others significantly (adaboost or random forests to mention some). Few years later, also ensembles in unsupervised learning were ...
4
votes
2answers
194 views

What are features that distinguish clustering, blind signal separation and dimensionality reduction?

In terms of input -> [process] -> output what are features that distinguish clustering, blind signal separation and dimensionality reduction? From this ...
6
votes
3answers
4k views

Supervised clustering or classification?

The second question is that I found in a discussion somewhere on the web talking about "supervised clustering", as far as I know, clustering is unsupervised, so what is exactly the meaning behind ...
1
vote
3answers
224 views

Can unsupervised evaluation measures for clustering replace a supervised evaluation measure?

Is it possible to have the same evaluation performances when comparing some clustering algorithms using many unsupervised evaluation measures instead of a supervised one ?
1
vote
1answer
348 views

Market / Customer Segmentation - Merging two different segmentations

I have a database where each observation is a person. They were questioned on their attitude towards the consumption of X category of product. I have being using K-means to segment this data. I have ...
7
votes
1answer
395 views

Cluster clickstream data

I've recently entered the realm of machine learning and a project I am working on requires me to cluster users based on the order they visited webpages on a website. I have data in the form of: ...
4
votes
4answers
1k views

Initializing K-means clustering

If I have a certain dataset, how smart would it be to initialize cluster centers using means of random samples of that dataset. For example, suppose I want 5 clusters. I take 5 random samples of say, ...
2
votes
1answer
151 views

Market segmentation based on a time of consumption

I'm an almost graduated applied math student. I do some sporadic work in marketing. I have done a few market segmentation projects. I am soon going to do one which is important to me. I usually ...
1
vote
2answers
568 views

Logging similarities between vectors with R

I'm trying to write a program that automatically groups similarities between vectors. The vectors are comprised of point coordinates. For example (assuming X, Y, and Z are numbers): Data Set 1: [1, ...
2
votes
2answers
694 views

Methods for comparing clustering results

I am doing an unsupervised clustering analysis for a genomics project. This means that I do not know when a particular clustering analysis is good or not. I am running different clustering algorithms ...
5
votes
3answers
900 views

Evaluation measure of clustering (without having truth labels)

I'm clustering a set of data but I don't have truth document that allow me to evaluate the result of clustering (I have unlabelled data), so I can not use an external evaluation measure. In this case, ...
3
votes
2answers
127 views

How to apply unsupervised classification to spatial data

I am trying to learn how to apply unsupervised classification to spatial data. In near infra red satelite pictures; the ocean is dark and the forest is white. Each pixel in such an image is an ...
1
vote
0answers
65 views

SOM automated/objective clustering

So as I understand it SOM is primarily a visualization tool and clustering is a logical next step after you construct a SOM from data. Typically, the clustering is subjective in that after looking at ...
8
votes
2answers
220 views

SOM clustering for nominal/circular variables

Just wondering if anyone is familiar with clustering nominal inputs. I've been looking at SOM as a solution but apparently it only works with numerical features. Are there any extensions for ...
3
votes
1answer
474 views

How can I assess how descriptive feature vectors are?

I am assessing how good different features are for unsupervised classification of a set of objects. For each different feature I test, I have computed a feature vector that describes the object. I ...
14
votes
1answer
6k views

How to define number of clusters in K-means clustering?

Is there any way to determine the optimal cluster number or should I just try different values and check the error rates to decide on the best value?
4
votes
0answers
247 views

Recommended method for finding archetypes or clusters

I wish to cluster users together in a database, with each user represented by a number of features that are both discrete and continuous in nature. The aim is to define a small number of archetypal ...