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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11 views

Hierarchical Tobit Model

I'm studying the effect of various criminal case and court district characteristics on sentence lengths. I was planning on running a hierarchical linear model (HLM) of individual defendants/cases ...
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20 views

using cluster information in multiple imputation

i need to impute a dataset all categorical variables before doing analysis. I can just simply impute with mode of all data or a variable. I belief that better option will be to classify the subjects ...
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1answer
15 views

What method to use for cluster identification ?

This question is from a confused novice. I have a data set with where each point is located in a 2-D space defined by two objectives (say, X and Y). I wish to identify a set of points from this space ...
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0answers
36 views

(Spatial) distance between cluster means

I'd like to cluster points based on a distance criteria. As I want to cluster spatial points I am using euclidean distance and a hierachical cluster approach. In a final step I'd like to cut the ...
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0answers
12 views

Cluster analysis of open-question responses

I am currently analysing a 1100-observation dataset of open-question responses. I was wondering whether I could really do a segmentation based on these responses. What I've done is so far is ...
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1answer
28 views

Is it posible to perform the inverse of multidimensional scaling analysis

We have lot of 3D data and we reduced it to 2D for performing fuzzy clustering and obtaining prototypes. We used some matlab functions that were very well documented. Now we would like to see to which ...
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1answer
69 views

Excel: which products are most frequently ordered together? (clustering question)

I'd like to recruit your help in coming up with an Excel-based method to analyse a set of raw ordering data where each item is on its own row. So, in the data below, order 111 contains two part ...
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0answers
20 views

Reducing high dimensionality as well as feature selection on multivariate time series

Lately I've been reading a lot about time series clustering as I want to search for similar patterns in my own data set. Even though I feel like I understand the basic concepts of this task I still ...
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0answers
5 views

Turning MiniBatchKMeans into Fuzzy MiniBatchKMeans

I'm using Scikit-Learn, which has an implementation of MiniBatchKMeans. I'm very inexperienced with ML, so I'm wondering how (if ...
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4answers
203 views

Clustering binary categorical data

I have some data where I have certain classes (c1, c2, c3, c4 ...) and the data comprises of binary vectors where 1 and 0 denote that an entry belongs to a class or not. The number of classes will be ...
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0answers
15 views

Spectral clustering using RBF Kernel function in R

I have extracted user-features and item features in my recommender system using a modified SVD approach built on ALSE (loosely based on Yehuda Koren's paper). I now want to cluster items not directly ...
2
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1answer
36 views

Which clustering technique to use for a temporal dataset?

I have seen a similar question but thought I'd ask my own to hopefully garner some usefull feedback. Basically, I have a large temporal dataset, consisting of domestic smart energy meter use ...
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1answer
16 views

Cluster Analysis Sample Size for Diss Proposal

My psychology dissertation will be a cluster analysis with one grouping variable. How do I know how many participants I need? I imagine there should not be more than 5 clusters. 79 items make up 9 ...
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0answers
16 views

Suitable plot for 5 dimensional feature vectors?

I have a list of personality scores obtained from 100 people, based on the Big-Five personality test. Each person has one score for each of the five assessed traits. I put these scores into a 5 ...
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1answer
17 views

Need help understanding response from Metis

I was wondering if any of you could help me understand the response I got from this clustering algorithm (Metis). As you probably can see, I'm trying to cluster IP addresses based on common records ...
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0answers
22 views

Anomaly detection for one feature vector

I have a $n$-dimensional vector of ordered multiple testing $p$-values and I would like to reject the first values that are under a certain threshold $\alpha$. I am looking at this problem as an ...
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0answers
18 views

Determine Number of Clusters Using Silhouette Method and Distance Matrix

I have a tree and its associated distance matrix. Now I want to cut the tree to give a desired number of clusters. Luckily there are a number of methods for determining the number of clusters as ...
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0answers
11 views

cluster analysis results variation across samples

I am performing cluster analysis with a relatively large data set (10 000 observations) and I am comparing the results obtained by different clustering algorithms. Because the computations are very ...
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0answers
25 views

Unsupervised Random Forest for Visual Codebook generation

I'm trying to apply the bag of visual words approach to make scene classification. I started to use k-means to generate my codebook, but rapidly discovered its limitations. From one codebook ...
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1answer
22 views

On the connection between SSE and absolute deviation from the centroids

Is there any connection between sum of squared error SSE and the absolute deviation from the centroids after clustering. More formally, I have clustered $T=\{x_i\}, i\in\{1,\ldots,n\}$ and the ...
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0answers
18 views

Performance of hierarchical clustering for binary data in R

I am trying to use Hierarchical clustering to see how well it performs in classifying a dataset which I previously know its true classification. I am new to clustering in general. I was able to draw ...
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1answer
47 views

Best metric for evaluation of mixture-of-Gaussian clusters on big-data

I have made a new algorithm that is specifically crafted for clustering very large datasets. In order to document it as a research paper, I have to choose one or two internal (no-label) cluster ...
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0answers
5 views

How to identify a subset of effects as the drivers of significant differences?

Okay I will explain this as best as I can. I have an additive risk score for each of my observations (i.e. I'm putting 100 individual effects into a single variable). The means of this risk score ...
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1answer
28 views

How to reduce number of points for clustering

Description I have to find number of clusters for 1D data. All clusters are assumed to have a gaussian distribution (so there is a big number of same points). I have a robust "aglomerative clustering ...
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1answer
27 views

Assigning meaningful cluster name automatically

The objective of my work is to cluster the text documents. Once the documents are clustered, traditionally the system will assign numeric value for the clustered group. For example if I have 5 ...
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1answer
30 views

Identifying subsets for outlier detection in local outlier factor

I am trying to gain better understanding of the idea of local outliers (as discussed in this pdf) and how the function is implemented. Here are the key passages from the pdf: Local outliers: ...
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1answer
32 views

Cluster analysis on time series samples

In the follow-up to this Ways to understand 2-dimensional time-series data I'm working on 2D time series data where two attributes are depth and temperature. When I plotted depth-vs-temp curve and ...
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24 views

Cophenetic distance matrix to a dendrogram

In hierarchical clustering procedure, a distance matrix is used to construct a dendrogram with an appropriate method of clustering. In the process of constructing a dendrogram, a cophenetic matrix is ...
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1answer
23 views

Extract (ultrametric) distances from hclust or dendrogram

How can the matrix of (ultrametric) distances be extracted from the result of hclust (or a dendrogram in general) in R? The ...
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1answer
28 views

K-means cluster Analysis and 4-point Likert Scales

Is there a concern using a 4-point likert-type scale (i.e., agreement) when attempting a cluster analysis using k-means clustering? Most of the data for the items in my data set are favorable (e.g., ...
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0answers
23 views

Clustering coefficient for a clique

I would like to understand how to solve this exercise about clustering coefficient for a clique. As shown in the picture below if node pairs (a; b), (a; c), (a; d), (b; c), (b; d) are linked, then the ...
2
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1answer
42 views

Selecting number of clustering classes automatically

I am working in text clustering. I would like to find a way to identify the number of classes for the clustering process automatically rather than proving the number of class manually. Is their any ...
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0answers
10 views

Heuristics for unsupervised or semi-supervised approaches to GIS coordinate data

I have a more conceptual/heuristic question about how to go about formulating a problem in order to take a semi- or unsupervised method of solving it. I'm working on a project with data collected ...
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0answers
24 views

Implementation for Co-Clustering

I am looking for existing implementations for co-clustering (aka biclustering). I came up with biclust function available in MATLAB, but still I am wondering if ...
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1answer
37 views

Combine Clustering and classification

I have a receipt database of a grocery store. I would like to find classes of similar customers based on their receipts and classify people after their shopping to one of these classes. Let us assume ...
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14 views

Deriving distance function based on multiple variables

This question is in relation to the following answer of yours: How to derive a distance function based on multiple variables for cluster analysis? Suppose I have 7 variables, each quantitative (and ...
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2answers
67 views

Feature / attribute selection for k-means or other clustering

It seems to me that in literature it is assumed that one knows which features / attributes to choose to characterize an item in clustering. If I have a database with items which have many attributes, ...
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1answer
40 views

Different hierarchical clustering results

I'm running a hierarchical clustering on a sample of data using the steps below: ...
2
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0answers
24 views

Good (2d) visualization of a mixture model clustering

I have a specific problem which I'm surprised I don't find answers on-line and I hope somebody here has a good suggestion for me. I'm working with a large data set which I'm clustering into specific ...
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0answers
21 views

BOW prediction of cluster for training data

I am creating a bag of visual words for classification of videos. I am not using SURF descriptors and that is why I couldn't use OpenCV's BOWImgDescriptorExtractor ...
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2answers
53 views

Cluster Data based on Distribution

I have a list of diseases for my research. For each disease, I have a list of ages for the diseases. "Breast Carcinoma" may be a list of [1,2,2,3,4,5,5,5,5,5] while "Adrenal Cortex Neoplasms" maybe be ...
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0answers
36 views

Computing a distance matrix between multiple multivariate time series

This question has also been asked on stackoverflow.com. Yet my aim is to ask for efficiency gains on the aforementioned platform. My aim here is the correctness of my approach. I am trying to cluster ...
2
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1answer
51 views

Is there a decision-tree-like algorithm for unsupervised clustering?

I have a dataset consists of 5 features : A, B, C, D, E. They are all numeric values. Instead of doing a density-based clustering, what I want to do is to cluster the data in a decision-tree-like ...
3
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1answer
43 views

Clustering without a distance matrix

I've recently completed a project where I used scikit-learn's DBSCAN module to find clusters in relatively short strings of text. I used a custom string similarity ...
1
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1answer
59 views

Clustering algorithms for extremely sparse data

I am trying to cluster an extremely sparse text corpus, and I know the number of clusters (my data is the title and author list of scientific publications, for which I already know the number of ...
0
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0answers
15 views

Fuzzy C-Means / Latent Dirichlet Allocation

I have to compare the results of document clustering via latent Dirichlet allocation and fuzzy c-means. How can I do this? I've got the option to compare the probabilities the documents are assigned ...
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0answers
45 views

Clustering on a data set with mixed variables

I have a data set consisting of $n$ elements with $d$ features for each element ($x_{i,f}$ means the value for the f-th feature of the i-th element). I would like to cluster this data set into $k$ ...
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0answers
8 views

Clustering with proportional threshold

I'm starting learning about clustering so perhaps this is a basic question. The idea is to generate clusters out of an array of floats, 1 dimension and N dimensions, get the mean value of each ...
0
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1answer
68 views

Outlier detection using clustering and dissimilarity matrix in R

I have some problems in finding the outliers using clustering. The data.frame is ~20000 observations and each row has mixed types of variables(numeric, nominal and binary). What I want to do is to ...
3
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1answer
154 views

Using PC scores or cluster analsis in predictions

I have very big data and low number of observations. So I decided to use PCA to reduce dimension of the data. The following is R example (just an dummy example - for workout): ...