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.]

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Creating Clusters with SOM ouptut in SAS [on hold]

I have 5 variables to I want to cluster the observations based upon these features. From SOM output in SAS Eminer, I have got 10*10(arbitrarily chosen) neurons with gradient colors for each of the ...
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11 views

Hierarchical cluster plot in rstudio [on hold]

i have a set of data with the following dimensions 6870 rows and 210 columns, after using the follow code to generate a cluster diagram i get the following Calculating distance ...
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1answer
12 views

DateTime Variable — Kmeans

Suppose I have a DateTime variable that I want to cluster on. I only really care about the day, hour, and minute. Is it wrong to create a column in my data set for each of these or should I keep the ...
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45 views

Combining AIC and BIC [on hold]

For my dataset of ~19K data points to cluster, I want to use a criterion to choose the number of clusters. BIC (Bayesian Information Criterion) gives too few clusters (~180) while AIC (Akaike ...
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1answer
19 views

How to select the best number of clusters in cluster analysis in SPSS?

When I used SAS for cluster analysis, I used to use some plots of CCC, pseudo F and pseudo T^2 indices to help determine best the number of clusters. Not sure about this in SPSS, not familiar with ...
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9 views

Replying the canonical discriminant analysis on the test set [on hold]

I run a canonical discriminant analysis following those 2 video lessons on Coursera. Now, I would like to test the accuracy of the clustering methodology on a test set by replicating the canonical ...
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1answer
33 views

normalisation in k means clustering on percentages and other numerical variables

I have several variables to include in k-means, some of them are percentages (between 0-1) and some of them are numerical variables (positive values). I know normalisation is required when the ...
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2answers
20 views

Similarity measure for clusterings in graphs evolving over time (temporal network)

From what I understand about clusters, they can be obtained from an existing graph at 1 instance of time. But consider the situation of a temporal network, such as a social network, where the graph ...
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71 views

Was it as valid to perform k-means on a distance matrix as on data matrix (text mining data)?

(This post is a repost of a question I posted yesterday (now deleted), but I've tried to scale back volume of words and simplify what I'm asking) I'm hoping to get some help interpreting a kmeans ...
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14 views

clustering based on string variables?

I am working on a project and currently experimenting cluster analysis. The dataset is mainly string variables, and I think my biggest three challenges in the data cleaning phase are the following: ...
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9 views

analysis of nationwide inpatient sample data [on hold]

Has anyone analyzed NIS data using R or SPSS? Is there code available for this analysis? I have a dataset ranging from 2003 until 2009 and am comparing three surgical groups. I am not sure how to ...
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16 views

What is the best way to cluster the gower similarity matrix?

I have a dataset that contains the gower similarity of each observation from each other. So the dataset looks like this: ...
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1answer
21 views

Clustering sets of vectors

I have a set of $d$-dimensional vectors $\{v_1,v_2,\dots,v_n\}$, each of which has been assigned a label from a set $S=\{s_1,s_2,\dots,s_k\}$. I would like to find another set of labels $T=\{t_1,t_2,\...
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21 views

How to measure clustering algorithm performance? [duplicate]

For supervised learning, both regression and classification have ground truth. The model performance can be measured against ground truth. For example, $R^2$ in regression or accuracy (0-1) in ...
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32 views

Choosing evaluation measure for non-parametric clustering

I have to cluster some data using non-parametric clustering technique which is given in this paper. After all the cluster evaluation measure used in this paper is Normalized Mutual Information as they ...
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8 views

LSA Clusters Have the Same Words

I use LSA to create 5 document clusters and identify top 10 words nearest to cluster centroid. I found 4 words appear in all clusters(good, stay, staff, clean) . How to deal with this outcome? Should ...
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1answer
15 views

How do I choose the appropriate numbers of customers to be considered for cluster analysis?

I am currently doing a customer segmentation project in SAS. I have identified 2700 customers who are have made a purchase in each of the 4 years I am analysing. For the cluster analysis the more ...
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17 views

how to measure clustering task with unlabelled data set [duplicate]

I wanna know, how to measure the accuracy of a clustering method when we deal with data set without an a priori knowledge about class belonging ? (the data used for the clustering task, do not contain ...
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4 views

Characterize clusters composition

The problem I am performing a two-steps clustering task on a large dataset (a dozen variables but several hundred thousands of observations) by first using k-means and then using a Hierarchical ...
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34 views

Clustering in Industry - Why only k-means? [closed]

How successful is clustering in industry as opposed to academics? More specifically, it seems like clustering algorithms geared towards mixed data type data sets aren't implemented by the common ...
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5answers
129 views

What is the best way to detect repetition in xyz data for purposes of splitting data?

I'll use this picture to explain What I want to do is define some patterns as trained patterns. Then given data I want to be able to determine if the pattern exists in the dataset, and if it does ...
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4answers
117 views

Can PCA allow to identify redundant variables that can be removed before doing cluster analysis?

I hope this is suitable for this forum: I am new to PCA and what I ultimately want to do is perform cluster analysis on my dataset. I have 20 physical descriptor variables for organisms, each with ...
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4 views

Hard partitioning of the association matrix

I obtain a co-association matrix $n \times n$ that corresponds to the maximum likelihood estimate of the probability of pairs of variables being in the same cluster. Further suppose that there are $k$ ...
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Moving Data From Scikit-Learn to Elki for Clustering [migrated]

I have 100,000 sentences that I've processed into TF-IDF vectors using scikit-learn's TfidfVectorizer with highly customized stopwords and nlp stemming. My goal is ...
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Does anyone have a good implementation of ENCLUS subspace clustering? [closed]

I am looking for an implementation (preferable Matlab) of the entropy-based subspace clustering ENCLUS. Instead of starting coding it, I would very much like to have one working. Thanks for any help!!
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29 views

Clustering of variables with mixed type [duplicate]

I have a dataset with 6 variables. Some variables are continuous numeric while others are discrete. The data has the following structure: ...
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22 views

cluster external validation [closed]

I am using ELKI in order to perform location clustering with DBSCAN and OPTICS. My data set include 30 participants but it is not labeled but I do have pair of coordinates (e.g. home, work, etc) as ...
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28 views

What is the definition of a kernel on vertices or edges?

I am currently trying to perform clustering on a collection C of undirected and unlabeled graphs. I decided to use to a kernel on graphs to obtain the kernel matrix of C. Then I can derive the ...
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220 views

Clustering of variables: but they are mixed type, some are numeric, some are categorical

I have a dataset with 15 variables. Some variables are numeric, continuous. Other variables are boolean, dichotomous (true/false). There's also one variable categorical, nominal. ...
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1answer
51 views

Instruct me about K-Means Clustering

I have been instructed by my supervisor to run K-means in Matlab on my data which is comprised of sensory data observations that pertain to 7 outcomes, which I have labeled using numbers from 0 to 7. ...
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70 views

factor model and large covariance matrices

Imagine the number of variables $\textbf{p}$ is 5 times larger than the number of observations $\textbf{n}$ and the sample covariance matrix is almost block diagonal (with low off block diagonal ...
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9 views

Grouping target variable into bins of minimal variance for uniformly-observed, seasonal timeseries data

I am looking at a key performance indicator that is measured uniformly over time for which I strongly suspect seasonality. I would like to create groups to identify periods of time where observations ...
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11 views

How to use WeightedCluster::wcKMedoids to provide clustering for heatmap or heatmap.2 in R? [migrated]

TL;DR: How to use the WeightedCluster library (the wcKMedoids() method in particular) as input to ...
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4answers
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Does “curse of dimensionality” really exist in real data?

I understand what is "curse of dimensionality", and I have done some high dimensional optimization problems and know the challenge of the exponential possibilities. However, I doubt if the "curse of ...
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1answer
14 views

Cluster evaluation in system that implement auto determine cluster number

First of all, how to know wheter document clustering result was "good" or "bad"? Not in ordinary k-means, but in algorithm that enable automatic cluster number, like g-Means. Came across Q&A ...
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1answer
8 views

Spatial indexing and grid based clustering for huge GPS datasets [duplicate]

This is my first time dealing with spatial datasets. I have a 2GB GPS dataset that contains Polylines and timestamps for taxi rides. It looks similar to this https://archive.ics.uci.edu/ml/datasets/...
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k-means++ algorithm and outliers

It is well known that k-means algorithm suffers in the presence of outliers. k-means++ is one effective method for cluster center initalization. I was going through the PPT by the founders of the ...
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14 views

Relation discovery between two time series data

I'm looking at analyzing the relation between temperature & sales/searhes of particular product at a daily grain The relationship is little complex, for example Sales go up for low temperature ...
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Simple English translations regarding thematic textual analysis

I have run a thematic textual analysis of economic select committee meetings in the UK using t-lab. I ran a thematic analysis of the E.C.U.s, and categorised the E.C.U.s into (for example) 5 thematic ...
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9 views

Defuzzification in fuzzy clustering

I am using fuzzy c-means algorithm to cluster my data. To give labels to data in the end, I choose the class which has the highest probability for each data point and assign that point to that class. ...
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1answer
19 views

Statistical Test for Embedding?

I have a bunch of labeled objects. Let's say there are K classes altogether. Now suppose every object is mapped to a data point in R^n, or in other words, is embedded in R^n. Ideally a good embedding ...
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Choosing a cluster with low variance and many data points

I have data points that have been grouped using k-means clustering. Some of these clusters may only have one data point, which would give them a variance of zero. But I am more interested in the ...
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1answer
27 views

How to differentiate two distinct linear populations with clustering in R

I am new to clustering. My apology if this has been asked before. I'd like to differentiate two distinct linear populations within sample matrix, and tag them differently. Apparently k-means couldn'...
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24 views

Using spectral or hierarchical clustering for small world graph

Suppose we have a weighted edge graph with small-world property and we want to cluster the nodes of the graph based on the edge weights as similarity of nodes (the weight of an edge shows the ...
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8 views

How can I cluster data points according to the local minima they belong to?

I'm using the genetic algorithm for hyperparameter optimisation. My loss function is the cross-validated loss, that means I can evaluate my loss function but I don't know how it looks like (the shape)....
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280 views

With categorical data, can there be clusters without the variables being related?

When trying to explain cluster analyses, it is common for people to misunderstand the process as being related to whether the variables are correlated. One way to get people past that confusion is a ...
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Applying DBSCAN to a huge GIS dataset with a Haversine distance metric.

I have a training set (2GB) that contains GIS trajectory data for multiple taxi rides. I want to cluster the final destinations based on their spatial density and have therefore been trying to use the ...
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17 views

Clustering on two feature spaces

I want to do a clustering task with my data to find some interesting patterns. I have two groups/vectors of features V1 and V2 which are fundamentally different and are supposed to measure different ...
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Z-score transformation for joint cluster analyses

I am dealing with a situation where I need to use a joint matrix of approximately gaussian distributed data (limits - Inf to + Inf) and beta-distributed data (both produced using different ...
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Analysising the difference between k-means and spectral clustering algorithm

My target is to cluster the spatial area based on the location(X,Y) and pollutant concentration(Z). So there would be three different attributes along the spatial area(n_sample = grid point) I have ...