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)

0
votes
0answers
9 views

Multiple Correspondence / Correlation Analysis

I'm a little bit new to statistics, so I'm not sure what I really need. I have a table like the following with some categorical/nominal values (like Gender and Age Group) and a ratio scaled value ...
0
votes
0answers
10 views

Best Measures for face detection [on hold]

I'm trying to evaluate series of face detection algorithm. For that I need the best protocol or measures to distinguish each algorithm. As output I only have the number of faces detected (until ...
0
votes
0answers
12 views

Clustering Matrix with binary values

I have a large matrix. It consists of about 10.000 rows (each row one document) and 10.000 columns (each column one word). The binary value indicates if a word exists (1) in the particular document or ...
1
vote
0answers
26 views

What form of analysis for exploring associations of classifications?

I have survey data where participants classify a list of things as one of five categories e.g. {A,B,C,D,E}. For example: ...
0
votes
1answer
24 views

Representative observations from the hierarchical clustering results

I'm using the hierarchical clustering to generate groups from unlabeled dataset (or observations). I’m using the matlab function ...
1
vote
1answer
19 views

DBSCAN: What is a Core Point?

I have a question about DBSCAN. The points here are classified as core points, border points or noise. A point p is a core point if at least minPts points are within distance ε of it, and those ...
0
votes
1answer
45 views

A question on cosine similarity & k-means

I used the following code to perform clustering of a dataset in R. distMatrix1 <- dist(sample2, method="cosine") km<-kmeans(distMatrix1,3) I have got some ...
-1
votes
0answers
25 views

How to find the 1000 closest point to a centroid built from another matrix

I actually work on text-mining. I try to find the 1000 closest documents (inside a corpus of 56000 documents) to a selected corpus of document (150). There are a lot of words in my dictionary. I ...
3
votes
1answer
57 views
+50

When does pam (partition around medoids) fails to find the optimal solution? (counter example?!)

If I understand correctly, the pam algorithm is a greedy search for a set of medoids such that no other set offers a lower cost (i.e.: the sum of distances of points to their nearest medoid). ...
3
votes
0answers
29 views

Is dimensional reduction using Autoencoders possible with a small sample size?

I have a data set that is not too big but high dimensional, let say 10000 dimensional. I want to use an autoencoder to extract relevant features (clusters) in the data. Usually when I have seen ...
-1
votes
1answer
20 views

clustering evaluation for a special case

In my dataset each point comes from one of 3 classes, so the true labels are like [0,1,0,0,0,2,1....]. I have to cluster them in 200 clusters. I want each cluster ...
1
vote
0answers
21 views

Cluster analysis of large, multiple answer dataset

I have to analyse data from a marketing study. I will use SPSS. The questionnaire will look like this: Q: Imagine Situation X. Select 1-3 Criteria from the list that best describe your feeling. ...
1
vote
0answers
16 views

Interpreting Silhouette plot

Can someone help me interpret this silhouette plot? The things that come up on my mind are: Some clusters are very small Orange cluster is very big Pink, dark green and light green clusters are ...
0
votes
1answer
34 views

Hierarchical Cluster Analysis: similar to market basket and how to implement in R? [closed]

This is a simple cluster analysis question, but I've not done much clustering at all, so any help would be appreciated. I have N items and there are M people, each picking some of these N items. I ...
-1
votes
2answers
37 views

How to calculate distance between points in DBSCAN matrix data? [closed]

I'm making a simple C implementation of DBSCAN following his pseudocode. If I well underand how DBSCAN works, I may represent my set of N elements (each with M features) with a NxM matrix. When it ...
1
vote
0answers
13 views

How to detect how clustered coordinates are in a graph? [closed]

Assuming we have a 2D graph with n points, I want to detect how clustered are the points. By clustered I don't mean only how close they are in general, but a very clustered graph can be one with m ...
1
vote
0answers
8 views

Clustering correlated variables

I have a dataset with 10 variables. Three pairs of these variables are correlated. What happens if I add all of these variables in the clustering? Does this depend on the type of clustering? I found ...
0
votes
1answer
28 views

Grouping usernames/emails in a data set

I have a column in a dataset that is the 'user'. Can look like this: tom green tgreen tomgreen@here.com sam blue samtha green How do i get this to group this so that the first three are grouped ...
0
votes
0answers
9 views

compare mixture models

In my department there has been a discussion going on whether there is a way to compare clusters derived from mixture modelling in something like goodness of fit or adequacy. after long discussions ...
0
votes
0answers
10 views

Randomized groups for A/B testing

I have the dataset where the dimension = 10 and number of samples = 20. Let's denote the features by $x_1, x_2, ..., x_{10}$. I'd like to analyze the effect of $x_2$ on $x_1$. I applied the following ...
0
votes
1answer
39 views

Proper dataset format for K-Means and DBSCAN clusterers

I'm trying to classify web traffic using clustering algorithms with my own C program, capturing packets with libpcap. In this article K-Means, DBSCAN and AutoClass ...
0
votes
0answers
6 views

How to compare multiple non-hierarchical classications of the same dataset

I'm a taxonomist working on an identification consistency project in which a couple dozen researchers were asked to manually classify the same set of images into like groups. Each classification will ...
0
votes
0answers
24 views

Clustering with restrictions

I have this dataset of a ranking of roughly 32.5k players in increasing order. The third column contains the number of players which have the corresponding score, whilst the second is the number ...
-1
votes
1answer
41 views

What is the probabilistic view on clustering? [closed]

Say that we got a set $\mathcal{X} = \{x_1, x_2, \ldots\}$ of samples. You want to partition $\mathcal{X}$ into $k$ subsets, where $k$ is unknown besides the fact that $k \ge 1$. How clustering ...
0
votes
0answers
13 views

Fuzzy c-mean clustering (FCM) [migrated]

From these two results for fuzzy c-mean clustering : ...
0
votes
1answer
29 views

Comparing kmeans cluster

I have 150 images, 15 each of 10 different people. So basically I know which image should belong together, if clustered. These images are of 73 dimensions (feature-vector) and I clustered them into ...
1
vote
2answers
41 views

What Algorithm to cluster web user sessions without knowing the number of clusters?

I created user sessions from server log data. Based on the URLs I categorized each server request according to the respective page content (e.g. topic_1 = main page, topic_2 = team members, etc.). The ...
1
vote
0answers
28 views

How would one use KDE as a one 1D clustering method?

I need to cluster a simple univariate data set into a preset number of clusters. Technically it would closer to binning or sorting the data since it is only 1D, but my boss is calling it clustering, ...
1
vote
1answer
28 views

When would I use EM instead of k-means?

When would I want to assign cluster probabilities to patterns instead of hard assignments to clusters? Can someone elaborate?
0
votes
1answer
16 views

Is there a difference between 1D Mean Shift and KDE for clustering 1 d data?

I need to cluster (or group) large one dimensional data sets into a set of fixed bins. I started out using K-means, but I want to look into other approaches. Two that I have found are Mean Shift and ...
0
votes
0answers
6 views

Can I conduct multilevel analysis with two aggregated data sources?

I have two data sources with completely different specific individuals. One contains individuals answering whether or not they have watched the movie "Lego", and the other date source contains ...
0
votes
0answers
5 views

Assigning new observations to already created clusters [duplicate]

I have created 6 segments in SPSS using the Two-Step Clustering approach based on about 2600 observations. I have now collected 1000 new observations and would like to be able to assign these new ...
3
votes
1answer
30 views

Clustering and A/B testing

My question is the following: Let's imagine I've defined clusters in my data (different segments of customers) and I run an A/B test. Can I compare the performances of the different clusters on the ...
0
votes
0answers
7 views

Cluster Component Coefficient Calculation for data in Rows

I have my data in the form such as: ...
0
votes
0answers
18 views

Finding cluster overlapping

I've a set of locations and I've clustered them thrice on the basis of some different parameters, now I want to find overlapping of the clusters obtained. So basically the dataset now contains list of ...
0
votes
1answer
31 views

Compare clustering results based on intra cluster similarity

I am working on a project for my university. A part of this project is to compare the influence of PCA on clustering. Therefore I have a football player dataset that contains a feature called ...
0
votes
1answer
23 views

Estimate group averages

If I have 100 numbers from two separate groups $X$ and $Y$. How can I manually estimate or derive an algorithm to automatically estimate $AVG(X)$ and $AVG(Y)$? I know all the numbers, but I don't ...
0
votes
1answer
23 views

Is it necessary to normalize data for hierarchical clustering of mixed variables using complete linkage?

I have a dataset with 3 numerical variables and 1 categorical variable which is binary (0,1). For clustering these data, should I normalize my numerical variables to the unit range (0,1) by ...
0
votes
1answer
97 views

How does Principal Component Analysis help me understand my data?

I have a dataset which contains 10000 examples. Each example has 100 dimensions. These dimensions have the same scale. I clustered all examples using their 100-dimensional vectors and drew the elbow ...
0
votes
0answers
6 views

Understanding the Biclust class in R [migrated]

I'm new in R Language, but I'm using the biclust package for Bicluster Analysis. After to search information in web, I could run some biclustering algorithms but I could not access to the resulting ...
0
votes
1answer
28 views

confusion matrix and Jaccard index computation in O(n) for cluster comparison

My problem: I have two clustering (or partitions) of a set of numbers $\{1,...,n\}$, each with $k$ cluster (or subsets), that i have to compare and, to do that, i'd like to try some different indexes ...
0
votes
0answers
22 views

DBSCAN application for detection of anomalous instants in a particular time series

I have time series/matrix with cpu utilization of 4 servers (about 17k points). I am trying to use DBSCAN algorithm to find out which server is operating suspiciously compared to other using the ...
0
votes
1answer
24 views

How do we put various multivariable data in cluster bucket

A data with multivariable of mixed types (Nominal and Continuous) are clustered using R package of Daisy/Agenes. How are we going to put the variables in the cluster bucket I was thinking to put max ...
1
vote
3answers
51 views

Is there a situation when one would use L1 norm over L2 norm in k-means algorithm? [duplicate]

Is there a situation when one would use L1 norm over L2 norm in k-means algorithm? In most of the articles online, k-means all deal with l2-norm. L1 norm does not seem to be useful because it is not ...
0
votes
1answer
13 views

Unsupervised clustering of households into types

Traditionally, households fall into a couple of discrete categories. For example: Husband and wife Husband, wife and young kids Divorced Wife and kids Bachelor Adult child living with husband and ...
1
vote
0answers
19 views

Log-likelihood distance

How to calculate log-likelihood distance between clusters in two step clustering? if the following is the solution,then how to proceed? I would appreciate if someone can help me to find this. I am ...
1
vote
1answer
58 views

What should be the ideal number of clusters for the plot whose image is given?

I have a dataset whose wssplot I've created but then not able to find any sharp elbow, so if anyone could please me with it?
1
vote
1answer
22 views

How can I simulate feature tolerances in DBSCAN to see how the clusters change?

I am performing clustering based on the DBSCAN algorithm on a 3-dimensional data set. After running the algorithm, I get X clusters as a result. What I want to do is to see how the clusters behave if ...
0
votes
1answer
38 views

What is the use of distance matrix in clustering algorithms?

I found a C library for clustering and I was reading about the distance matrix here: it says: The first step in clustering problems is usually to calculate the distance matrix. This matrix ...
1
vote
1answer
19 views

Exploring distribution of pairwise distances before clustering

I'm trying to perform clustering on a 200+ feature dataset consisting of brain measures for 200 healthy controls and 200 schizophrenia patients. However, I have the feeling the data points do not ...