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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Correlation or clustering of continuous score and discrete variable states

I have an experiment that produces a decimal score representing quality, and a bunch (5-30) of variables that each take on one of a set of discrete states. - The states are not meaningfully ...
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0answers
5 views

How can I determine local minima from a Kernel Density Estimation?

I have a fairly large 1-dimensional array that I am trying to cluster. I came across several other questions on this site where the top answer is to use a Kernel Density Estimation and then locate ...
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1answer
19 views

Mixing probabilities in mixture models using EM

Assume we want to estimate the mixing probabilities ($\pi_{k}$) for each member distribution in the mixture model. We know that $\sum_{m}^{K}\pi_{m}=1$, so we can formulate the optimization problem ...
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2answers
40 views

Using k-means with other metrics

So I realize this has been asked before: e.g. What are the use cases related to cluster analysis of different distance metrics? but I've found the answers somewhat contradictory to what is suggested ...
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0answers
20 views

breakpoint analyses on multiple series: how to detect common points

I have 20 time series that span the same period (100 days each), from 4 species sampled at 5 different location. I made a loop to perform a breakpoint analysis on all of them, resulting in 0 to 3 ...
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1answer
27 views

How to Calculate silhouette coefficient in SPSS for clustered data set?

I am having a pre clustered dataset with data and the action cluster identified for it using a custom clustering method. I am looking to calculate silhouette coefficient on this clustered dataset ...
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2answers
40 views

What's a good way to mentally visualize n dimensions in a k means

I've been using k-means to do some clustering and one of the ideas I'm struggling with is the n dimensions aspect. If I were clustering housing prices vs sq. feet its just a simple 2d graph. That I ...
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1answer
18 views

Is my understanding of how to calculate the reachability distance in local outlier factor correct?

Reading lof implementation at : http://www.cse.ust.hk/~leichen/courses/msc-it5210/lectures/LOF_Example.pdf the local reachability distance is given as : I don't fully understand this equation as ...
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7 views

Clustering techniques when there are two distinct but numerically similar clusters among others

I'm trying to cluster some spatial statistics for some behavioral estimation. However, parts of the trajectory report exactly 0 speed, which implies some kind of resting state which is different from ...
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1answer
13 views

Why is it called Epsilon for DBSCAN?

The two parameters of DBSCAN are epsilon and minimum samples. Shouldn't epsilon be called like "Circle radius"? Why is it called epsilon?
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18 views

How to measure the similarity of k-means clustering using different datasets?

I run k-means clustering on my dataset (100 samples in total) and partition the data into k=5 clusters. Then I want to test how robust of the k-means can be; however, I haven't got more new data ...
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8 views

automatic assign class name based on text

My question is , I have a set of plain text , i want to create category based on the text. Eg: i have written something about Soup recepie then the algorithm must create a category called Food. After ...
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14 views

Need a rigorous statistical framework for automating visualization

I am faced with a challenging problem. Suppose I have a large dataset with many attributes and I can filter the data using a set of attributes. The problem is in the event we have a large number of ...
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0answers
29 views

Order and similarity measurement

Let's say I have 10 different groups, and each group has its own string sequence. So, it should be like: ...
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0answers
20 views

How can I evaluate the accuracy of a clustering when I don't have information on the true class labels?

Already classified data set for the t-shirt factory problem I want to calculate the accuracy of my algorithm. I have the training data without any size information and I couldn't find the classified ...
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0answers
43 views

Clustering method that can use graph links, discrete and continuous properties?

I have an un-weighted, directed graph that clusters ok using MCL or other graph clustering algorithms. However, I also have additional discrete and continuous properties of the nodes being clustered ...
2
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0answers
28 views

Co-occurrence statistics for sets

I am looking for help in the following situation: I have a set of numbers $A=\{1, 2, \dots, 100\}$, and I am drawing subsets of 10 of these numbers $\{a_1, a_2, \dots, a_{10}\}$ according to an ...
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2answers
82 views

How does one go about clustering data?

(I have updated the question following conversation with @whuber in the comments). My case is as follows: I have around one thousand row vectors of dimension $1 \times 8$. These row vectors are ...
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1answer
25 views

How many factors should be used for a cluster analysis?

I have a short question. I've performed a principal component analysis and obtained two components. Are two components enough two perform a cluster analysis (number of participants > 400)? Thanks for ...
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1answer
52 views

How to determine which variable or combination of the variables are affecting to the predictor variable?

I have one dependent variable name as "win ration" of the deal contested and more than 30 independent variables, all are categorical variable name as role of the customer, geo, region, and 27 ...
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1answer
27 views

NbClust package r error

I try to do: NbClust(city, diss =d, distance = NULL, min.nc = 2, max.nc =5,method = "kmeans", index = "all", alphaBeale = 0.1) with city is a list of number ...
4
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1answer
112 views

how to discard values that are far from center of cluster in mixture model

I am trying to fit a bivariate cluster model with X and Y. What I would like to do is discard (make not clustered / un-grouped) that are far from the cluster center (for example $\mu$ + 2*standard ...
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0answers
17 views

Hierarchical clustering of repeated measures in a few locations

I have a water quality data (value) measured 10 times (every month - data) on three depths (shallow, medium, deep) in five location (A, B, C, D, E): I want to find in which location values are ...
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1answer
28 views

Clustering Two Variables With Disease Information

I was proposed a problem and I am not quite sure how to go about it. The problem is I want to find a relationship between two variables. For the simplified case there are only two variables, lets say ...
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2answers
47 views

How to fit mixture model for clustering

I have two variables - X and Y and I need to make cluster maximum (and optimal) = 5. Let's ideal plot of variables is like following: I would like to make 5 clusters of this. Something like this: ...
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11 views

R skmeans package - where does this error come from: “missing value where TRUE/FALSE needed” [migrated]

I tried to cluster my data in accordance with the manual provided by the skmeans packages's manual page I started by installing all required packages. I then imported my data table, and made a matrix ...
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1answer
25 views

Spherical K-means Clustering in R

I have a large data set that I would like to cluster using spherical K means algorithm. However, I am relatively new to this subject and R in general. Most of my knowledge is self taught and I am ...
4
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1answer
37 views

how to complement the results of cluster analysis with known groups

I have some prior knowledge of grouping, but this may be incorrect or is not sufficient as I need larger number of groups (i.e. subgroups). For example in the following data I have 3 groups in ...
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2answers
147 views

Clustering a noisy data or with outliers

I have a noisy data of two variables like this. ...
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1answer
101 views

What are basic differences between Kernel Approaches to Unsupervised and Supervised Machine Learning

I got nice graphical representation of Machine learning for clustering / classification. Source: Kernel Approaches to Unsupervised and Supervised Machine Learning by Sun-Yuan Kung Here are my ...
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1answer
27 views

Weighting related attributes in hierarchical clustering

I have two questions about output of hierarchical clustering and improving the output. I'm trying to learn more about performing hierarchical clustering in R so I started looking at a simple dataset ...
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0answers
8 views

How to implement optimal scaling of categorical variables for fuzzy clustering?

I am referring this paper "Fuzzy e-Means Clustering of Mixed Databases Including Numerical and Nominal Variables" from IEEE for implementation and learning. Kindly correct my understanding so far: ...
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0answers
25 views

How is predictor importance in a cluster analysis (in SPSS) affected by dichotomy and multicollinearity?

I want to use a cluster analysis (CA) in SPSS to define different profiles in my dataset. I am using different continuous variables for this, including several neuropsychiatric measures. I am new in ...
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0answers
33 views

I find very different results using a k-means or two-step clustering method. How is this?

I want to use a cluster analysis (CA) in SPSS to define different profiles in my dataset. I am using different continuous variables for this, including several neuropsychiatric measures. I am new in ...
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1answer
85 views

calculating probability or filtering that certain subject is not in the particular cluster

I have a situation where there are n individuals and p features (variables). I do have their cluster information. Here is an example: ...
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2answers
91 views

What algorithm does ward.D in hclust() implement if it is not Ward's criteria?

The one used by option "ward.D" (equivalent to the only Ward option "ward" in R versions <= 3.0.3) does not implement Ward's (1963) clustering criterion, whereas option "ward.D2" implements ...
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1answer
28 views

How can I make clusters with highly right skewed data?

My histogram of expected income is as below. As you can see, income is highly right skewed. I want to divide individuals with regard to income - for example into Low, Middle, and High, and then I ...
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0answers
43 views

Profile variable in collaborative filtering

I'm trying to create a recommendation system based on purchases. I did some tests and I found that for some groups of customers, the recommender works very well, but not for others. How can I ...
1
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1answer
29 views

Clustering spherical-shaped data

I have a data set that consists of census data (5 attributes). Performing PCA, I found that the original data looks like a big chunk (please look at the first picture), and therefore, I decided to use ...
1
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1answer
29 views

Finding optimal combination of parameters for clustering

I have a spreadsheet with one object per line. Each column contains values that are parameters of my objects (let's say length, width, height, weight, color). I can classify objects based on color and ...
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2answers
58 views

K means clustering inadequate in determining extreme regions in R

I want to identify the regions that are considerably higher than the highest cluster. (The obvious regions which should be identified as their own clusters, notably at the x coordinate ~10 e+07. How ...
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2answers
29 views

Suggestion for discovering inherent patterns in data

I have a a big data set of clients with all sorts of variables that describe their background, payment history, and more... I also have a subset of those client who all have portrayed similar ...
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1answer
47 views

identify different points [closed]

I have a large scatterplot, with about 100,000 (x,y) points. The x coordinate is the set of numbers from (1 to ~100,000) - in other words, no 2 points have the same x-coordinate. The y is mostly ...
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3answers
26 views

Computing Image Similarity based on Color Distribution

Image Similarity based on Color Palette Distribution I am trying to compute similarity between two images based on their color palette distribution, let's say I have two sets of key value pairs as ...
1
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1answer
38 views

How to choose the right distance matrix for clustering?

I am attempting simple Ward type clustering. However, the R package is proving several choices to use for the distance matrix. I am wondering how I am supposed to determine the right distance matrix ...
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2answers
42 views

k-means vs k-median?

I know there is k-means clustering algorithm and k-median. One that uses the mean as the center of the cluster and the other uses the median. My question is: when/where to use which?
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0answers
5 views

Finding correlation of attributes based on known decisions

If there are two matrix of data, rows are all a person (under 50 users). In the first dataset, columns are attributes (over 500 columns) of a user with the rating 0 for does not exist, 1 for exist. ...
0
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1answer
41 views

How to test for correlation between frequency of an event and the stock market

I am currently running an event study, for which I need to find out if my events are clustered and/or if their frequency is tied to the stock market. Creating a scatter plot of the event dates and the ...
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0answers
20 views

clustering for histogram shapes

I am trying to get a start on a clustering problem. The sample data is trade volume at a particular price. Some notes about the data: number of bins vary from sample to sample (larger price range ...
4
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3answers
129 views

K-means cluster analysis with K=2 as a binary classifier

I used two variables, height and weight, and using K-means cluster analysis with $K=2$, two clusters were obtained. I used $K=2$, as the observations either belong to men or women. I then compared the ...