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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7 views

Applying Multiple Correspondence Analysis when predictors have thousands of levels

I apologize in advance if my english isn't too clear. Please feel free to leave a comment and tell me what part doesn't make sense. I'm currently working on a dataset which contains web data and I ...
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1answer
21 views

What are the best metrics for examining the separateness of clusters?

I'm working on a new clustering approach. My algorithm has not any intra-class comparison; it only uses some inter-class comparison to decide whether the iteration continues. This lack of criterion ...
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0answers
6 views

Estimate Epsilon in DBSCAN with k-nearest neighbor algorithm

Following DBSCAN paper (quote below), I'm trying to develop a simple heuristic to determine the parameter Epsilon with K-nearest neighbors (k-NN) algorithm. For a given k we define a function ...
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2answers
35 views

How to select or validate the selection of a clustering method?

One of the biggest issue with clustering is that we may happen to have to derive different conclusion when base on different clustering methods used (including different linkage methods in ...
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0answers
10 views

How to calculate BIC for K-Means to get best K

I'm really new to K-Means clustering technique. I'd like to calculate BIC for K-Means to find best K (number of clusters). I looked around on the web to find a solution in python but there is no ...
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27 views

Find best K value for K-Means clustering [on hold]

I'm doing the K-Means clustering for my data. Following python script does K-Means for 2 clusters. ...
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0answers
6 views

Comparison of Clusters with the Aggregate [on hold]

Just wanted to know if there were any good tests/measures that compare a cluster to the aggregate data. So suppose that I already have clustered/partioned data $x_{i,j}$ (data point j in cluster i). ...
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9 views

How to find routes of buses? How can I make an algorithm that can follow a trail of gps points [on hold]

If I have a number of reports [time, current_latitude,current_longitude, bus_name, going_to, coming_from] of buses that where seen at a place (mostly bus stops). How can I find the route of each ...
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1answer
35 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 ...
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0answers
40 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: ...
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1answer
30 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 ...
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1answer
20 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 ...
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1answer
50 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 ...
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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 ...
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1answer
70 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). ...
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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 ...
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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 ...
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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. ...
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17 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 ...
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2answers
40 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 ...
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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 ...
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0answers
9 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 ...
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1answer
29 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 ...
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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 ...
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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 ...
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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 ...
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7 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 ...
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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 ...
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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 ...
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0answers
13 views

Fuzzy c-mean clustering (FCM) [migrated]

From these two results for fuzzy c-mean clustering : ...
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1answer
30 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 ...
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2answers
42 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 ...
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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, ...
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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?
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1answer
17 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 ...
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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 ...
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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
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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 ...
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0answers
7 views

Cluster Component Coefficient Calculation for data in Rows

I have my data in the form such as: ...
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19 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 ...
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1answer
33 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 ...
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1answer
24 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 ...
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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 ...
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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 ...
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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
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...