Partitioning data into subsets of objects according to their mutual "similarity," without using preexisting knowledge such as class labels. Clustered-standard-errors and/or cluster-samples should be tagged as such; do not use the "clustering" tag for them.
1
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0answers
145 views
How to choose clusters from variable clustering (varclus) procedure?
I am attempting some variable reduction before I perform a logistic regression. I am quite interested in using Hmisc::varclus in R. However, I am having some ...
0
votes
1answer
143 views
k-means and different metrics [duplicate]
In presentations of k-means to compute the centroid of a new cluster the Euclidean average seems to always be used. If the similarity metric used is not the Euclidean metric it seems to me this other ...
1
vote
2answers
220 views
R: Visualizing document clustering results
I have a k-means clustering result with 35 clusters, there are 5000 documents that each belong to one of the 35 cluster. I would like to visualize the results of the clustering algorithm on a scatter ...
1
vote
1answer
45 views
Non-exclusive categorical model
I have a project to model users with characteristic tags (e.g. runner, cyclist, swimmer, vegan, pianist) in order to correlate user behaviour to these labels. Obviously a user can have multiple ...
0
votes
0answers
36 views
Choice of population to study
I want to do classification or clustering of my big data set on web applications.
I would like to cluster website visitors who are identified by their cookie ... which they can drop whenever they ...
1
vote
1answer
80 views
Can high quality clusters be found in every data set?
In my data I have 3 clusters with average silhouette 0.61 and very few negative values. I repeated k-means 10 times with k ranging from 2 to 10. This seemed to work ok, but the problem is that I got ...
0
votes
1answer
29 views
Cluster detection in a distribution [duplicate]
Possible Duplicate:
Determine different clusters of 1d data from database
I am trying to detect clusters, i.e. segments on a linear scale, with visibly higher concentration of distribution ...
0
votes
1answer
55 views
Where can I find resources about X-Means Clustering?
Is there resources where I can find out more about this method, it was proposed by a client and there doesn't seem to be much on this.
Most of the searches seem to only turn up information about ...
0
votes
1answer
159 views
KL divergence or similar “distance” metric between two multivariate distributions
I have a large dataset composed of many samples; each sample is as follows:
imagine a grid indexed by i,j
for a sample k, I have Y_k, where Y_k(i,j) is the probability density for k at (i,j)
of ...
0
votes
1answer
75 views
Manual computation of Gower's similarity coefficient
There is an example on the computation of Gower's similarity coefficient on the page,
Gower's similarity coefficient
I am trying to work out the similarity manually between patient 1 and 2, however ...
4
votes
3answers
189 views
Why doesn't k-means give the global minimum?
I read that the k-means algorithm only converges to a local minimum and not to a global minimum. Why is this? I can logically think of how initialization could affect the final clustering and there is ...
1
vote
0answers
36 views
Comparing ballot data from two consecutive elections
I have two datasets of general elections (in a multi-party single-vote system) which I'm trying to analyze.
Each has about 10,000 data points (ballots) with some 30+ features - the main features are ...
1
vote
2answers
392 views
Interpreting result of k-means clustering in R
I was using the kmeans instruction of R for performing the k-means algorithm on Anderson's iris dataset. I have a question about some parameters that I got. The ...
3
votes
1answer
120 views
What are the use cases related to cluster analysis of different distance metrics?
I'm trying to use different distance metrics like Euclidean, Manhattan, cosine, chebyshev among other distance metrics in my k-means algorithm to calculate distances between the data points and the ...
5
votes
1answer
118 views
Binary classification of DNA motif sequences (bioinformatics)
I've been working on on a method for binary classification of DNA
sequences. In more detail, here is what the method does.
Given a family of DNA sequences, for example DNA sequence
motifs, I try to ...
1
vote
2answers
66 views
Clustering and variable selection
Let's say I'm trying to cluster $n$ points in $\mathbb{R}^p$, and I know in advance that only $s$ many of these $p$ dimensions determine the differences between the clusters. Of course, I don't know ...
0
votes
1answer
52 views
Units for silhouette measure
While drawing graphs that compare the silhouette measure of different clustering algorithms, what unit should I specify for the silhouette width?
1
vote
1answer
180 views
Does Mahalanobis distances have “significance” associated with them?
I have a "distance matrix". let's say a 6x6 distance matrix, each cell is the Mahalanobis distance of two "clusters" (or sets/groups of things in a multidimensional space),
I want to "count" the ...
1
vote
1answer
88 views
Cluster analysis with skewed distibutions
For my master's thesis I would like to use different clustering algorithms to cluster municipalities (as objects) in regard to their land-use characteristics (as variables).
Analyzing my data ...
4
votes
1answer
104 views
Index plot for each cluster sorted by the silhouette
After a cluster analysis I´m trying to plot for each cluster the Index plot of the Silhouette value instead of for the complete dataset
(like in the WeightedCluster Library Manual by Matthias ...
0
votes
0answers
108 views
improving the performance of simple Kmeans clustering algorithm
I'm using WEKA- Simple K-means clustering algorithm in order to get the efficient and accurate pattern for my unsupervised training data. My training data is a medical (diabetes) data that consists of ...
0
votes
0answers
40 views
Data grouping algorithms?
I have numerous one dimensional vectors, $V_1,...,V_i$. Each vector is of variable size composed of natural numbers from different unknown distributions. I'd like to find a way to group/cluster values ...
2
votes
0answers
50 views
Appropriate threshold to map a similarity value to an edge in a graph
In order to cluster users given a user-item binary matrix data, I am planning to first find user's similarity (Jaccard) and then use graph theory to isolate clusters (communities). I need to map the ...
0
votes
1answer
110 views
Density Clustering
I am looking for a clustering algorithm. My idealized dataset looks like this:
The clustering result should look like the Rapidminer density plot:
Means 3 or 4 clusters should be the clustering ...
1
vote
2answers
123 views
Clustering large scale data in fine-grained clusters
I've got large amount of data (e.g. 100K) and I want to cluster them in very fine-grained clusters (e.g. 10K). I look for an appropriate algorithm that uses the similarity function instead of whole ...
-2
votes
1answer
82 views
How to work with dummy variables and other variables at the same time in cluster analysis [closed]
How to do cluster analysis if you have all variables in array from 1 to 5, and 1 dummy variable (0 and 1). can you explain from the beginning--Excel format, please.
0
votes
2answers
206 views
Cluster analysis or regression?
I have a table with each row representing single printer model, its features, and price. I want to know how price is formed based on these features. What should I start with? Multiple regression, so I ...
0
votes
0answers
30 views
How to preform 1NN with single centroid per class in SAS?
I've computer a single centroid per class using PROC fastclus in SAS,
...
0
votes
1answer
92 views
Equal Euclidean distance of a single data point to all the Cluster Centers
In K means Clustering, suppose, if there exists equal euclidean distance of a data point to all of its k cluster centers, which cluster the data point will choose to become its member?
Is there any ...
2
votes
3answers
136 views
How to use data analysis output (e.g. clustering) in predictive regression?
I performed some data analysis and visualizations on my dataset and found there are likely $k$ clusters present. How can I use this in a predictive regression setting?
My first thought is to create a ...
2
votes
1answer
80 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 ...
1
vote
1answer
124 views
Using mixed variables in two-step cluster analysis
I need to perform cluster analysis on my data set which includes both continuous and categorical type of variables. Having read around, I think K-Means is not a suitable technique for mixed data. Can ...
2
votes
2answers
151 views
Dimension reduction for sparse matrix for clustering
I'm looking for a Sparse matrix dimension reduction. I already used some feature selection methods like PCA but it doesn't give me good results. I want to apply mixture models for clustering my data.
...
3
votes
0answers
132 views
test the significance of clusters
Good morning,
I am analyzing a dataset composed by 364 subjects and 13 binary variables (0,1 = absence,presence).
I am testing possible association (co-presence) of my variables. To do this, I was ...
2
votes
0answers
37 views
Spectral Clustering of Graph
I am trying to cluster the graph using spectral clustering. However I am unaware of the number of classes that exist in the data.
Will it be a good idea to do PCA on the adjacency matrix to find ...
1
vote
0answers
149 views
Determining Optimal Number of Cluster in Hierarchical Clustering in Consideration of Variance of Data
I'm applying a Hierarchical Agglomerative Clustering (HAC) for grouping my data and I need to determine the number of the cluster automatically. To determine the optimal number of cluster, I obtain ...
2
votes
1answer
194 views
Does SPSS rescale dendrograms?
A colleague and I have been clustering some data in SPSS (v19) and R (2.15), respectively. Using the same distance metric and agglomeration method, we get identical merge orders/agglomeration ...
1
vote
1answer
133 views
Clustering time series
I want to create forecasting for a large quantity of time series. Since they are too many, I am thinking on reducing my data by clustering it into to similar groups. However, I am using SPSS modeler ...
2
votes
1answer
112 views
Does the average distance in K-means have to be monotone decreasing?
I'm implementing the k-means algorithm myself. I don't see any obvious mistake in my code and it seems to work well. However, there's something I don't understand.
My algorithm, working on vectors ...
2
votes
1answer
230 views
Convergence of K-means
I have a clustering algorithm which works iteratively like K-means, but there are some constraints on cluster sizes with lower and upper thresholds.
Do you know any convergence proofs of K-means in ...
2
votes
2answers
277 views
Clustering high-dimensional sparse binary data
I am trying to cluster Facebook users based on their likes.
I have two problems: First, since there is no dislike in Facebook all I have is having likes (1) for some items but for the rest of the ...
2
votes
3answers
280 views
Which type of regression fits better?
I am a newbie in data mining world. I have a general question.
I have a data set which has 10 independent variables and one target variable named as category which has 9 values like: 1, 2, 3, 4, 5, 6, ...
3
votes
0answers
74 views
What are pitfalls of bootstrapping on random sample of master data?
Will I obtain seriously biased results if I use bootstrapping on a subsample of a larger dataset?
Rather than drawing 100 bootstrap samples from a dataset of 50 million + records, which could hog ...
1
vote
0answers
19 views
Curve based clustering of multivariate data (time series like data) [duplicate]
Possible Duplicate:
Reducing no of variables subsetted based on depth for PCA
I have a question, I am trying to apply a method to my research area, which has not been aplied yet, based on ...
1
vote
0answers
43 views
Sparse estimated data recommender system
Premise:
For a product and a user, the system has to recommend him/her other non-users related with him/her that are most likely to be interested in that same product.
Available data includes:
...
1
vote
1answer
54 views
Shape analysis of an object to create features for pattern recognition
I am currently with a medical imaging project. Just wondering how to measure the shape of a sphere. For example, how to give a measurement that an object is more like a sphere than the other?
I know ...
1
vote
1answer
97 views
Reducing no of variables subsetted based on depth for PCA
First of all, sorry for the strange title, I had no idea how to describe my problem better.
My issue is the following, I think it is pretty much limited to geosciences.
I have several properties for ...
2
votes
2answers
58 views
Relevance of overall absolute values in covariance analysis of two variables
I am performing K means clustering on a gene expression dataset.
I am aware of the fact that the Pearson correlation metric allows to group trends or patterns irrespective of their overall level of ...
0
votes
0answers
24 views
Analysis of Systematic differences in two multifactor pricing models
I am trying to compare two different pricing models for a product. The two models take the same inputs ( 10-12 different factors)and i know the definitions of both functions which calculate the price. ...
1
vote
1answer
94 views
Mahout Scability
Do you know any real world examples of how much Mahout can scale? I wonder how much it can scale in collaborative filtering, clustering, and classification ?
