Cluster analysis is the task of 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.]

learn more… | top users | synonyms (1)

-1
votes
1answer
23 views

K-means with learning proces

I have a data set in which I already know the cluster to which each individual belong just by empirical observation but I want to predict, given the characteristics of a new individual in which ...
0
votes
1answer
64 views

Log likelihood in EM Algorithm

I try understand the log likelihood in weka. I read about that is a probabilistic metric, but i cant understand, if is better when have low value or high value? How i can get the likelihood value, ...
1
vote
4answers
106 views

Deterministic clustering approaches

I need a deterministic [in the sense - robust to the ways of initial input / initial seeds] clustering method to group values in distributions that could be either random, normal or log-normal. ...
0
votes
1answer
22 views

Segmentation of Geodemographic Data

I have a dataset that has 5 variables (columns). These are median house price, median income, number of people with no educational degree, number of people with high school degree, number of people ...
0
votes
1answer
134 views

Market Basket Analysis using Clustering to discover *new* product combinations

I have transaction data from a Quick Service Restaurant (QSR) client. Each record in this data set represents a transaction. My objective is to discover products that are the best candidates to be ...
1
vote
2answers
54 views

Comparing performance of kNN and kMeans

How do they compare performance wise in speed, accuracy, sparse, dense dataset ? Is it possible to somehow theorize what the runtime for kNN or means would be?
0
votes
1answer
28 views

Using browser version types/numbers in Analysis in R

I am doing some analysis using survey data. The target variable is a customer satisfaction metric. It would be helpful to find what versions of what browsers, are causing low customer satisfaction so ...
0
votes
1answer
79 views

Standardization before PCA with data in same units and similar interval? [duplicate]

We have 16 variables which are indices produced by calculations based on ratio (unitless in fact). Some examples of the ranges of our variables are (0.450-0.750), (0.000 - 0.800) and (0.000 - 1.000). ...
2
votes
0answers
45 views

Spectral clustering or hierarchical clustering for this senario?

I have a data set of about 40,000 time series. The length of each time series is 64. I consider these 64 as features for the data. I want to cluster data into groups which have similar time series (I ...
0
votes
0answers
37 views

K - means clustering - visualize for multiple class

I clustered seperate dataset containing data from different classes. I want visualize all the clusters in one plot to see how much they overlap or if they even do overlap.
1
vote
1answer
29 views

Classifying unlabeled data, but with cost function

I need to classify objects with ~50 features into 3-4 different classes, there are no labeled examples. Moreover there is no absolutely correct class for any object. However I do have cost value for ...
0
votes
0answers
29 views

How to perform cross validation clustered input data?

How do i perform knn cross validation with input data that has been clustered using k-means. I seem to be unable to find the correct function which is able to do so. ...
0
votes
0answers
62 views

How to combine Euclidean and Cosine distance?

EDIT (No duplicate of Converting similarity matrix to (euclidean) distance matrix): This question is centered on asking how to combine values from Euclidean and Cosine distances obtained from not-...
1
vote
0answers
11 views

Label reduction on dataset

This question related to this other one, for which I have devised a strategy and now want some feedback on it. My data consists of 434042 rows, each corresponding to an observation tagged with 1 of ...
1
vote
1answer
154 views

R - How to fix NbClust error with error message: “The TSS matrix is indefinite. There must be too many missing values.”

I would like to know how I can use clustering methods in R (in this case, Kmeans) if I have an "unkind" input matrix (I get this error log: The TSS matrix is indefinite. There must be too many ...
0
votes
1answer
36 views

Model-based clustering evaluation with BIC

Let's say I have fitted two models using EM-clustering and they differ in both the number of clusters and are fitted on different subset of features (chosen from the same set of features). Could I ...
0
votes
0answers
63 views

2 Different F1-Measure to calculate clustering performance - which one is correct and why?

I know it sounds incorrect but that is the truth Here let me show you This below one is the first one and very widely used in the literature First one reference : Steinbach, Michael, George ...
1
vote
1answer
20 views

Does Newman clustering work on weighted graph with non-integer weights?

I have a weighted undirected graph, where weight is distance and it is between 0 and 1. I want to apply the weighted version of Newman clustering. I think weight must refer to strength or similarity, ...
0
votes
0answers
10 views

How to predict new data goes which cluser in R [duplicate]

I already have k means output and i have segmented my users accordingly. Now, I have to predict cluster number for new users whenever they come. Do I have to run kmeans each time a new user comes into ...
3
votes
1answer
25 views

Fourier transform clustering

Which clustering algorithm would you use, moreover which distance measure, in case of analysis in frequency domain? I would like to perform Discrete Fourier Transform on time series and perform ...
2
votes
1answer
18 views

How to build a distance function given a cluster of points?

Given a non-elliptical cluster of points in a n-dimensional space I would like to get a distance function from the centroid of this cluster such that its "equipotential" surfaces has the same shape as ...
1
vote
2answers
45 views

Clustering methods ⊂ Unsupervised learning

Is it proper to say that clustering methods are mostly unsupervised learning techniques, with some exceptions such as model-based clustering?
1
vote
1answer
89 views

Stata: How to plot groups of variables side-by-side in stacked percent bar chart with subgraphs? [closed]

I did a cluster analysis of categorical variables and want to plot the result in a summary graph. There are three groups of variables that contain 'dummy variables'. I'm able to plot one group of ...
0
votes
1answer
22 views

Data to use for cluster analysis

I have a data frame of employees hours at work. The variables are time coming to work, time going home (finishing work), and time worked for the day, which is not the difference of going home and ...
2
votes
0answers
20 views

Rearrange 2D grid [closed]

I have a 2D grid on which I represent data points: Here, the red data point activates the grid on positions (1,1), (1,4) and (2,3). The blue data point activates the grid on positions (1,1), (4,3), ...
0
votes
0answers
16 views

Categorical Clustering of Users Reading Habits

I have a data set with a set of users and a history of documents they have read, all the documents have metadata attributes (think topic, country, author) associated with them. I want to cluster the ...
0
votes
0answers
27 views

Clustering Spatial Data while Maximizing a Constraint

I'm trying to perform a spatial clustering assignment by minimizing spatial distance while maximizing total weight within each cluster. My Data My data contains 3 columns and approximately 170 rows ...
1
vote
1answer
59 views

Cluster Boostrap with Unequally Sized Clusters

I need to perform a bootstrap for variance estimation on a GEE model for clustered data that I am analyzing. I understand that I need to use a clustered bootstrap for this, which is pretty much the ...
0
votes
1answer
50 views

How to combine the results of several clustering with scikit-learn?

I am trying to fit several cluster algorithms on one or across several subsets of a data matrix X, of shape (n_samples, n_features). For example: ...
0
votes
1answer
27 views

What is the relation between linkage and hierarchical clustering

I am self studying hierarchical clustering and got confused about the concept of linkage, could anyone explain what does it mean? what role does it play in what type of clustering? Any input will be ...
0
votes
1answer
22 views

In what way does clustering help in classification?

I understand that with a K-means or DTW algorithm one can cluster time series using a distance criterion, i also understand that with a K-NN algorithm for example one can do pattern recognition and ...
1
vote
0answers
33 views

Joint probability distribution function between different time-series clusters

I have 24-hour time-series data-sets for Solar Power and Power Consumption respectively for an entire year i.e 365x24 data set. Intuitively, the data set captures the variation of each of the ...
-1
votes
1answer
46 views

proper Similarity measure and clustering algorithm for binary data

data sample as follow : interest to find clusters of similar users, pages number around 100 pages. users around 1000 , i would like to know what are proper Similarity/dis. measure can used in this ...
0
votes
1answer
17 views

Algorithm that partitions a set coordinates around X number of fixed centroid coordinates?

I understand that in K-means you select how many clusters you want and the result is the location of each centroid. How does one handle a situation when you know how many and where each centroid is, ...
3
votes
1answer
49 views

Useful type of clustering method

I have a set of points in $R^3$ whose volume is increasing as time goes by. They tend to be clustered but I don't know how many. Also, the number of clustering might be changing when new points enter. ...
1
vote
0answers
44 views

Why is it that a larger 'k' value fails to converge but a smaller 'k' converges?

I'm doing clustering via GMM, which is initialized first by k-means. I am using a data matrix that cannot be classified as small by any standards, they are usually of the size ...
1
vote
0answers
27 views

merge small clusters in R [closed]

In R, I have cut a dendrogram into clusters. However some of the clusters have only few samples. How can I merge the small clusters with nearest big cuter. ...
3
votes
2answers
45 views

Cluster real numbers

I have a set of precise measurements, and what I want to do is count the frequency (how many time it appears) for each value. The problem is that these are very precise measurements and with a naive ...
0
votes
1answer
23 views

What quality measures can be used to evaluate a density-based clustering algorithm?

I have a weighted undirected graph, where weight is the similarity and range from 0 to 1. I applied a density-based clustering method and get some clusters, with overlapping nodes (node can belong to ...
0
votes
0answers
19 views

Need Help in Sampling

Can anyone help me understand the sampling method that needs to applied for this study? I am struggling with Straified Multistage Sampling and Stratified Cluster Sampling. The study is on foreign ...
0
votes
0answers
14 views

Similarity search in chromatographic data sets

Introduction I am doing research on ranking chromatographic data with respect to their similarity. I have several difficulties in getting started with a formulation of my problem. The data is given ...
1
vote
1answer
42 views

Clustering to minimize variance and maximize frequency in the cluster

Doesn't matter what clustering algorithm it is. Lets say I have data in a 2 Dimension space. (X Y). I want to tune (select parameters in) my clustering algorithm so that I minimize the variance in ...
3
votes
0answers
36 views

Topology of Confidence Intervals

I hope this is the right site to post this. The example I have in my mind is a GLMM model, where we infer random effects, and a random effect caterpillar plot (with confidence intervals): Now, ...
0
votes
2answers
88 views

Use a combination of grand mean and group mean centering to standardize variables

I'm using cluster analysis to examine profiles of three variables, X1, X2, and X3. Because ...
1
vote
2answers
41 views

Clustering methods that take data order into account

Is there any clustering methods that allows to take the time information (i.e. data order) into account ? That is, in addition to maximising intra-cluster similarity and minimising inter-cluster ...
0
votes
0answers
13 views

Dirichlet group assignment

As in the Dirichlet clustering, the dirichlet process can be represented by the following: Chinese Restaurant Process Stick Breaking Process Poly Urn Model For instance, if we consider ...
0
votes
3answers
113 views

Suspicious results after clustering

I've done a clustering and I think that my results are too good to be trusted. Here is my pipeline: Inputs: a dataset of 208 images, distributed into 2 classes (99 and 109 images in each class). ...
1
vote
1answer
90 views

Clustering crime data which has {latitute, longitude, crime-type} tuples

I have a data set which has thousands of rows of {latitute, longitude, crime-type} tuples. Sample Data: ...
3
votes
0answers
44 views

Can SVD be used to perform factor analyis?

What is the relationship between SVD and factor analysis? How can use singular values and other matrices from SVD to perform factor analysis or cluster document-term matrix without using other ...
0
votes
1answer
23 views

Is there a method to map clusters created for two independent data sets with certain common parameters?

Was thinking of a problem, but not yet clear on the exact statement.Please excuse the vagueness. The general idea I have might be explained using the following example: The two sets of data are ...