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.

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10 views

Functional clustering with R

I have a time series data in R, and I am using functional clustering. I would like to interpret a figure that is output below the code. Furthermore, I would like to control line colors and thickness ...
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0answers
17 views

Cluster analysis in uniform data

I have 261 vectors with 9 attributes. Each attributes contains numbers between 0 and 1. I am not sure what the most appropriate clustering method for this kind of data is. Initially, I used the ...
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0answers
24 views

SKLearn Clustering: how would you cluster a LARGE database of dogs? [on hold]

Given: A VERY large dataset of dogs. columns: ID (alphanumeric) Weight (numeric) Height (numeric) Eye Color (alphabet) ... (numeric) Tongue Length (numeric) How do you find what makes these ...
2
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0answers
25 views

What clustering algorithm should I use for clusters spaced on a grid?

I have some data sets of clusters of points arranged more or less on a regular grid. The data sets have these properties: The data is two, three, or maybe rarely four-dimensional. I know in advance ...
2
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2answers
35 views

K-means - comparing solutions with SSwithin elbow-method: minimum “too early”

I am running a k-means clustering process in R and I'm comparing cluster partitions of different number of clusters: k = from 1 to 17. Using the elbow-method, I have a minimum at k=5, but this value ...
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1answer
27 views

Clustering using gower distance in R

I have a dataframe which has categorical and numeric variables. I want to cluster this data using gower distance and get cluster values as a vector as in kmeans function. How can i achieve that?
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0answers
26 views

How to deal with empty values in a cluster analysis

I'm currently working on my master's thesis. Part of the work is a customer segmentation by means of a cluster analysis. One variable for the cluster determination should be the chronological ...
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0answers
28 views

Average linkage clustering

I have a matrix with proximity values $$ \begin{matrix} &1&2&3&4&5\\ 1& 1 & 0.9 & 0.1 & 0.65 & 0.2\\ 2& & 1 & 0.7 & 0.6 ...
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1answer
15 views

If two domains measure the same thing how to approach a cluster analysis?

I would like to perform a cluster analysis on my sample with a set of variables categorized in several domains. This is just fine but my problem is that two domains (one consisting of four and one of ...
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1answer
30 views

Interpret the visualization of k-mean clusters

Following my posted data here, I conducted a k-mean clustering analysis. I refereed to this post: How to produce a pretty plot of the results of k-means cluster analysis? for the clusters ...
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1answer
15 views

Clustering sequence on similarity using percentage identity matrix

I have a set of 400 nucleotide sequences and want to cluster them on basis of similarity. For clustering, I am expecting a similarity <=45% among members of a cluster. Also, there will be a few ...
2
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1answer
28 views

A non parametric clustering algorithm suitable for high dimensional data

What are suitable clustering algorithms for high dimensional data, where I do not have to input a predetermined number of clusters?
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0answers
50 views

Data Preparation for Cluster Analysis

Updated answer to "Data Preparation for Cluster Analysis": Based on the discussions, data normalization and removing correlation among data are often recommended. References posts: 1) Are mean ...
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4answers
312 views

How I can convert distance (Euclidean) to similarity score

I am using $k$ means clustering to cluster speaker voices. When I compare an utterance with clustered speaker data I get (Euclidean distance-based) average distortion. This distance can be in range of ...
2
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2answers
50 views

K-means: Why minimizing WCSS is maximizing Distance between clusters?

From a conceptual and algorithmic standpoint, I understand how K-means works. However, from a mathematical standpoint, I don't understand why minimizing the WCSS (within-cluster sums of squares) will ...
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0answers
9 views

rearrange similarity matrix for blockwise heatmap plot [closed]

I have a 10000x10000 similarity matrix with elements ranging from 0 to 1. I know by prior that the similarities have a blockwise structure if I rearrange the rows and columns accordingly and properly. ...
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0answers
8 views

Suitable classifier for 'objects on a string' data [closed]

I don't know if this can be considered a subjective question, but I have no option but to ask someone. My problem: I have a series of strings (or lines, think metal strings, not text strings) on ...
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0answers
26 views

how to cut Dendrogram using linkage distance and how to read it?

i tried to make clusters of monthly rainfall data over my studied area. i found it pretty difficult as to determine how many cluster i should have since i am new in statistics. from my linkage ...
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2answers
46 views

Dig deeper on “Determine the Number of Clusters and Validate It”

Updates to this thread: Based on Anony-Mousse's comments on my current results, there is only one big cluster in my data set. However, I think it might still be possible to reveal the clusters if I ...
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3answers
93 views

How to find the line?

Today I faced what I think is a very simple problem, but could't solve. I have this plot (data is below) with(mydata, plot(x, y)) It's clear that there are ...
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1answer
20 views

Incorporating new words in tfidf feature-vector for online clustering

I am building an Online news clustering system using Lucene and Mahout libraries in java. I intend to use vector space model and tfidf weights for Kmeans(or fuzzy/streamKmeans). My plan is : Cluster ...
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1answer
24 views

PSO Clustering using R using Repplab package

I wish to try clustering a matrix of numerical data using swarm intelligence. (Matrix is 28000 X 53 and sparse). I'm working in R and found the REPPlab package and used the EPPlab function. My ...
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1answer
20 views

Complement learning

First off, complement learning is a term I made up, not sure if it really exists. Given that the ground truth consists of 2 classes: class 1, class 2, and also two observed sets: oset 1, oset 2, such ...
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0answers
35 views

Supervised clustering/classification in a streaming environment

I am faced with a challenging problem and I was wondering whether someone could point me in the right direction of existing research literature. The problem is the following: Given a stream of data ...
2
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2answers
28 views

Finding cluster number based on distance & max element count

Given two constraints: The maximum distance d an element can lie from a cluster centroid (or medoid) The maximum number of elements n in one cluster Is it possible to find the minimum number of ...
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0answers
17 views

Clustering before or after ordination

Can someone explain the implications of performing clustering either before or after performing NMDS? I have some ecological data and I am performing a clustering analysis to identify communities of ...
1
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1answer
55 views

Measure the similarty between two sequences of letters

I'm trying to measure the similarity between two time-series sequences of letters with different lengths (e.g. s1=[A;A;A;C;B], s1=[Q;A;A;A;A;A] ). The order is very important. (e.g. s3=[A;A;A;C;C;C;C] ...
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1answer
57 views

Case-control Matched Clustering in Generalized Estimation Equation (GEE) (R:geeglm)

Question: I have matched case-control data and I would like to take advantage of that in my GEE analysis. In the standard approach to GEE analysis, we call each subject a cluster and fit ...
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2answers
48 views

What is second eigenvalue?

I am trying to understand Proc Varclus. This page says "2.If the second eigenvalue for the cluster is greater than the specified cutoff, then the inital cluster is split into two clusters." What is ...
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0answers
13 views

Difference between PROC VARCLUS and factor analysis

I understand factor analysis and hierachical clustering well but PROC VARCLUS is new to me. I read in this paper that PROC VARCLUS is a combination of Factor analysis and Hierachical clustering. Can ...
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0answers
15 views

Cluster of Cluster analysis across correlated longitudinal data

I am intrigued by the Cluster-of-Clusters approach (implemented via the bioconductor package ConsensusClusterPlus) and would like to apply it to my data matrix. However, I am not sure how appropriate ...
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0answers
36 views

Affinity Propagation (sklearn) - strange behavior

Trying to use affinity propagation for a simple clustering task: ...
0
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0answers
29 views

analysis of change and clustering

I want to do an analysis of change over two time points for a sample of children who move from primary to secondary school. Some children will move home, but all have moved school. Predictors: home ...
2
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3answers
29 views

Cluster similarity matrix as energies

I have a symmetric score matrix and I would like to cluster the values in two dimensions rather than through a tree. Is there any method / library that would take the input matrix and treat the scores ...
0
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2answers
22 views

Matrix clustering based on a Jaccard distance cutoff

I'm trying to figure out how to group elements of a binary matrix based on a given Jaccard distance cutoff. For example, suppose that I have information on the types of food carried by various grocery ...
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0answers
13 views

Statistical test for cluster independence?

1) If I want to test whether the cluster in red at the bottom figure here is a statistically separate cluster from nearby ones, what do I use? MANOVA? Anything else? 2) Any suggestions how to ...
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0answers
9 views

Statistical distances and time series of distributions clustering

I am interested in clustering $N$ time series of $T$ 'values' each. These values are distributions (which can be represented by their cumulative distribution functions (cdf), or their probability ...
0
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0answers
28 views

mean square distortion of quantization data set

I am using the matlab function lloyds to cluster a 1-dimensional timeseries. [partition,codebook,distor] = lloyds(training_set,initcodebook); and I get that the ...
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0answers
12 views

Representing clustered sequences in PCoA

I'd like to represent a set of clustered dna sequences (at a 0.0049 threshold) under a PCoA. But I have to calculate a distance matrix once the clustering done. How could I do that since the result ...
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0answers
13 views

Clustering timestamped data

hi I have data timestamped based on entry and exit of several people in the same house the problem is not to classify these people but to actually make a clustering on the variables input / output to ...
1
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0answers
14 views

Unsupervised clustering with Pearson correlation as measure of similarity

I have data from the cell lineage as samples: Cell1 -> Cell2 -> Cell3 Then for each of the above samples I obtained the expression for 'coding' and ...
0
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1answer
30 views

Analysing the spread of data of a variable

In my data set there are repeated measures where each subject is measured at three time points.I want to see how the data of a single variable is spread.That is to find groups so that I can categorize ...
4
votes
3answers
261 views

Text Mining: how to cluster texts (e.g. news articles) with artificial intelligence?

I have built some neural networks (MLP (fully-connected), Elman (recurrent)) for different tasks, like playing Pong, classifying handwritten digits and stuff...additionally I tried to build some first ...
1
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2answers
31 views

Cluster analysis missing data

I have a question about cluster analysis. Normally when there is less than 10% missing data and its missing at random, then it can be ignored. But how should I handle the missing data for a cluster ...
-1
votes
1answer
16 views

How to statistically categorize a list of reasons?

I am working with call center data, one of the variables available is "Reason" which is a description of the reason the customer called. There is 40 different reasons that the agent can choose from. ...
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0answers
7 views

Affinity Propagation with missing data

I'm using Affinity Propagation to cluster some data, and I have to deal with missing values, so for points that I have the data, I can use it to change similarity between them, but for those that I ...
0
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1answer
21 views

Clustering matrices with “2d interpretation”

I am not sure if I can formulate this such that it is clear. :) I have around 700 80x80 matrices, where each matrix shows some weather event (a matrix has continuous entries from 0 to 60). Now I ...
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0answers
33 views

Finding the number of cluster - categorical and numeric data - prototype analysis (R, weka)

In psychology many variables are categorical. Sometimes we can assign a number to the categorical data, it might work for binary variables, it's either 0 or 1, but there are many categorical variables ...
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0answers
17 views

Streaming K-medoids

Mahout, Hadoop machine learning library, contains an implementation of Streaming K-means algorithm that is based on the following paperworks The Effectiveness of Lloyd-Type Methods for the k-Means ...
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0answers
8 views

What is the best practice to deal with NA values when calculating a dissimilarity matrix?

I need to calculate a matrix of distances between sites where different variables were measured. I will use it in a cluster analysis. The following is a sample of the matrix I am dealing with: ...