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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14 views

2$\times$2 confusion matrix for clustering in R

After using the k-means cluster algorithm in R I want to generate a 2$\times$2 confusion matrix of the results. Do you have any recommendations? The only confusion matrix algorithms I have found are ...
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0answers
13 views

Determining k in k-means clustering by community detection in graph

I am faced with a problem of choosing an appropriate number of clusters in highly dimensional data. I've read many approaches to determine the number of clusters, and finally came to a solution and I ...
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0answers
18 views

Non parametric clustering by discretizing continuous IV

My final target is to develop a predictive model for a rate (fraction) DV. The DV showed bimodality and I have no variable that separates the two modes. Hence I created an IV using two observed IVs ...
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0answers
18 views

Structural Stability of Hierarchical Clustering

I am interested in some papers and reports about analysing the following problem: Assume, we have a stream of objects and a defined similarity/distance measure to calculate similarity/distance between ...
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2answers
38 views

Does clustering need scalar data?

I am trying to cluster 43,000 individuals on about 50 variables. The data contained in the variables are minutes of a radio shows which people listened to in the range of 0 - 3,000,000 minutes. My ...
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28 views

How to compare and cluster sets of daily time series?

I have multiple dataframes each representing traffic speed for each day of the year (366 dataframes for 366 days of the year). The raws of the dataframe are timestamp from 00:00 to 23:55 at 5 minute ...
2
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1answer
19 views

Clustering Consumer data with over 100 variables and 50000 rows each

I am tasked with performing a clustering exercise for a consumer survey dataset with the hopes of finding distinct consumer segments. In the past, I've done it using a variety of techniques- ...
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17 views

Problem on clustering

I have around 45 variables in a dataset of 500,000 rows. When I look at my variables, only 10 of them are quantitative - policy premium, age etc. and rest 35 are categorical - State, Smoker/Non ...
2
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1answer
19 views

Using an asymmetric distance matrix for clustering

I'm implementing a clustering task over a precomputed distance matrix. There are several distances I can use for the pair-wise distance matrix, some of them are not a metric (not symmetric). Can ...
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0answers
12 views

clustering verifying two basic invariance properties

disclaimer: I already asked something similar on stack overflow, but it seems to be a better place for that question here. I recently became interested in axiomatic definitions of clustering, cf. ...
2
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1answer
9 views

Can I use HCA-Ward's cluster-centers to run a K-means including a new item, to see to which cluster is more similar to?

Thank you for reading my question. I have an archaeological case-study, that we can call "Site1", that I want to compare with 9 others "Sites" studied by other scholars. For all of them I have 8 ...
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73 views
+50

Analysis of hierarchical clustered hospital data

I am hoping to get some advice from this excellent community on how I might try to proceed with an analysis of patient outcomes for a large conglomerate of hospitals. Essentially the dataset that I ...
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12 views

Design effect in Cluster Sampling [on hold]

I am facing a problem in calculating sample size of survey. When I am adding design effect this in calculation than sample size become increased. Why design effect in important for calculating sample ...
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19 views

Finding patterns in individual behaviour [closed]

I have a data set of 3000 individuals and 5 measures, which measure individual behavior (M=3000x5). I would like to find patterns present in this data set, for example common behavior organisation ...
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1answer
28 views

Determining characteristics of peaks after mclust finite mixture model

I'm working with the mclust package in R (specifically using densityMclust). As output, I have a file with mixing probabilities, variances, and means for each ...
-1
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1answer
29 views

Which K-mean algorithm I have to use for this problem?

Perform a k-means Clustering (non-iterative algorithm) using k=2 randomly initialised centroids (cluster prototypes), and the Euclidean distance. At the moment I manage to understand you can use ...
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1answer
13 views

Separating one group from other samples where the other samples may not belong to the same population

The way I see it, this is somewhat of a modified clustering problem. Let's say I have 1000 samples where the majority all follow the same behavior since they are from the same population. A number of ...
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1answer
21 views

Comparing / quantifying clusters

I have 'n' observations which are classified in two classes: Class A and Class B. The observations are mis-balanced with Class A constituting around 90% of the samples and Class B around 10%. The ...
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0answers
13 views

Fuzzy clustering of temporal data with constraints

I am looking for an algorithm to cluster timestamped events using the weighted elapsed time between the events as the only dimension. There are different types of events. There are constraints saying ...
76
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2answers
4k views

How to understand the drawbacks of K-means

K-means is a widely used method in cluster analysis. In my understanding, this method does NOT require ANY assumptions, i.e., give me a data set and a pre-specified number of clusters, k, then I just ...
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34 views

Gower's Distance issue

So, I'm relatively new to using Gower's distance to do cluster analysis. I've done some research on this for a little while and like the fact it can incorporate categorical variables. To get a better ...
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1answer
40 views

Learning a classifier to compute distance between points for clustering

I have dataset of items and want to cluster them. However, I don't have a predefined distance function. Does it make sense to learn a classifier that can predict the similarity between any two items? ...
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0answers
9 views

Merging linkage results from separate datasets for the purpose of clustering

Suppose I have a dataset P, This dataset is separated into smaller disjoint subsets Q_i. Since the clustering algorithm which has to be applied is space O(n2), the amount of data in P such that it ...
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1answer
19 views

How to detect noisy entries in the data set

I have a data set (entries described by the list of features X1-X7). This data set contains a small percentage of noise. How can I detect those entries that are subject to noise and exclude them from ...
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0answers
10 views

Technique to cluster with multiple dimensions

I have a data set which has respondents categorized as Tier 1, Tier 2 and Tier 3 - scores of their preferred genre of content and keywords relating to the genre. What I would like to achieve is to ...
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0answers
9 views

Bisecting K-mediods [duplicate]

Is there an algorithm like Bisecting K-mediods and what would its advantages/weaknesses be? It seems to me that it could be used well in combination of Dynamic Time Warping for clustering time ...
0
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1answer
28 views

Help with cluster analysis. Is this possible? [duplicate]

I'm working with the productivity of various government documents. In my data I have two variables (annual frequency and time of resolution) and each document gets its position in a Cartesian plane ...
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2answers
46 views

Bisecting K-means using Dynamic Time Warping

I'm trying to cluster time series of different length and I came up to an idea to use DTW as a similarity measure, which seems to be adequate, but the thing is, I cannot use it with K-means, since ...
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43 views

Gower's dissimilarity measure and Ward's clustering method

I have read some topics in this web side that, it is not true to use Gower's dissimilarity matrix for Ward's clustering algorithm. I have mixed type variables, first I had a dissimilarity matrix ...
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0answers
17 views

Implementing Hopkins and Cox-Lewis index in R

I was trying to implement some clustering tendency tools in R, namely the Hopkin's index and the Cox–Lewis index. Here is the link at page 901 to show what they are This is what I managed to come up ...
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0answers
20 views

Normalizing regressors in logistic space

I have a bunch of sklearn sgd models that have beta coefficients in the logistic space. I want to see if these models cluster ...
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2answers
126 views

Dynamic Time Warping Clustering

What would be the approach to use Dynamic Time Warping to perform clustering of time series? I have read about DTW as a way to find similarity between two time series, while they could be shifted in ...
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1answer
22 views

Exploring multiple semantic clusters of a given set of terms

I have a list of N object categories(e.g. apple, cell-phone, horse, chair, watch). Are there any methods of obtaining various clusters based on attributes of these categories ? For example, one ...
0
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1answer
14 views

K-Means Clustering on Distributed System

Can anyone explain how the k-means clustering algorithm converges on distributed systems? It seems that each node in our hadoop cluster would simply find a local optimum. How do we update across ...
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26 views

How to use binary variables in K means/Hierarchical clustering in SAS/R?

I need to use binary variables( values 0 & 1) in K means. But K means works with only continuous variables. I know some people still use these binary variables in K means ignoring the fact that k ...
2
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2answers
41 views

Cluster analysis as a preliminary analysis

I want to produce four groups (high/high, high/low, low/high and low/low) using two continues variables and compare these groups in terms of a few dependent variables. I know that cluster analysis ...
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1answer
29 views

classification of the groups [duplicate]

Let's say I have two variables: height and weight. I want to produce 4 groups: high height/high weight, high height/low weight, low height/ high weight and low height/low weight. I also want to see ...
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4answers
159 views

Are there any non-distance based clustering algorithms?

It seems that for K-means and other related algorithms, clustering is based off calculating distance between points. Is there one that works without it? Thanks!
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2answers
20 views

Revealing structure of clusters in a dataset

I have obtained values for the same parameter in various locations and I want to cluster them (abundance fraction of different minerals in a hyper-spectral image). These fractions have spatial ...
0
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1answer
37 views

Update Rules in Expectation Maximization

I am emulating a certain PDF behaviour using a function. However, due to divergent improper integral, I don't have a closed form expression for the normalization constant. To get the PDF, I just ...
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2answers
78 views

Mahalanobis distance measure for clustering

Let's say I have a group of clusters. Would you recommend Mahalanobis distance measure for checking if new arrived data belongs to existing clusters or it is an outlier? Also, would you recommend ...
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2answers
47 views

Use of bootstrap in clustering algorithms

Are there clustering algorithms that take advantage of bootstrap? For example can one combine bootstrap with a standard K-Means algorithm to scale K-Means. I was thinking if the following at a ...
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0answers
9 views

How to segment/profile users after performing classification?

So let's say that you've used logistic regression with demographic factors to make prediction on user bebaviour (e.g. those who clicked on a particular link v those who didn't) and you are satisfied ...
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10 views

Conjoint analysis

I am planning to conduct a conjoint study, but am having problems in terms of the number of product profiles to include. I will have to interview respondents in the markets and hence wanted that the ...
0
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1answer
18 views

Benchmarking hard clustering results against soft clustering results (ground truth)?

Well, I don't have labels (ground truth) for my data points. However, by domain knowledge, I am certain that a particular soft clustering algorithm will produce satisfactory results. Hence, I may use ...
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2answers
76 views

Mutual information for two soft clustering results?

I understand that mutual information (MI) of two distributions $X$ and $Y$ is defined as In the case of clustering analysis, say we are looking for two clusters out of 3 data points. We have two ...
4
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1answer
86 views

Clustering into teams of fixed size

There is a particular team-based video game that exposes a ladder of individual ratings for each player that looks like this (player, rating, wins, losses): A, 2000, 35, 12 B, 1900, 41, 19 C, 1800, ...
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1answer
57 views

Anomaly detection: multivariate Gaussian distribution

I am trying to do anomaly detection on a heterogeneous dataset (There are unknown groups present in the dataset). I want to try multivariate Gaussian distribution based approach, but I was thinking of ...
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0answers
50 views

Find the radius of a cluster, given that its center is the average of the centers of two other clusters

I do not know if it is possible to find it, but I am using Kmeans clustering, and I am stuck to the following. In my implementation, I create with two different threads the following clusters: ...
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0answers
23 views

Clusters as input for classification

I'm currently performing clustering as a batch job and then in real time I'm assigning new points to cluster whose centroid is closest to new arrived point. The other approach that I see is to ...